In [11]:
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from sklearn.preprocessing import MinMaxScaler
import warnings
warnings.filterwarnings("ignore")
In [12]:
from pmdarima import auto_arima

cols = ["Total Energy Consumption, Agriculture, Forestry, Animal Husbandry and Fishery(10000 tons of SCE)", 
        "Total Energy Consumption, Manufacturing(10000 tons of SCE)", 
        "Total Energy Consumption, Manufacture of Computers, Communication and Other Electronic Equipment(10000 tons of SCE)",
        "Total Energy Consumption, Wholesale, Retail Trade and Hotel, Restaurants(10000 tons of SCE)"]

Arima_Results = []

for col in cols:

    Target_Column = col

    df = pd.read_excel("D:\Jupyter Directory\Dissertation\Dissertation Datasets\Ones I will use\Consumption by Sector.xls")

    df.set_index("Database：Annual",inplace = True)

    new_df = df.T[["Indicators",Target_Column]].reset_index(drop = True)

    new_df = new_df.rename(columns={"Indicators": "Year"}).sort_values(by="Year")
    new_df["Year"] = new_df["Year"].astype("int")

    new_df.dropna(inplace = True)

    # Generate synthetic time series data
    t = new_df["Year"]
    y = new_df[Target_Column]

    # Find the best ARIMA model using auto_arima
    stepwise_fit = auto_arima(y, seasonal=False, trace=True)

    # Fit the best ARIMA model
    best_order = stepwise_fit.get_params()["order"]
    model = stepwise_fit.fit(y)

    # Forecast the next 10 data points
    forecast, conf_int = model.predict(n_periods=10, return_conf_int=True)

    # Append the forecasted values to the original data
    extended_t = np.concatenate((t, np.arange(t.iloc[-1]+1, t.iloc[-1]+11)))
    extended_y = np.concatenate((y, forecast))

    Result = pd.DataFrame({"Year":extended_t,Target_Column:extended_y})
    Arima_Results.append(Result)

    # Plot original data and forecasted values
    plt.figure(figsize=(10, 6))

    # Plot original data in a solid line
    plt.plot(t, y, color="dodgerblue", linewidth=2, label="Original Data")

    # Plot forecasted values as a dashed line with shaded confidence interval
    plt.plot(extended_t[-10:], forecast, linestyle="dashed", color="darkorange", label="Forecasted Values")
    plt.fill_between(extended_t[-10:], conf_int[:, 0], conf_int[:, 1], color="darkorange", alpha=0.1)

    # Set labels and title
    plt.xlabel("Year")
    plt.ylabel(Target_Column)
    plt.title(f"ARIMA Forecast (Order: {best_order})")

    # Add grid lines
    plt.grid(True)

    # Add legend
    plt.legend()

    # Show the plot
    plt.show()
    


Final_Arima_Results1 = pd.concat(Arima_Results, axis=1)

Final_Arima_Results1.columns.values[0] = "year"

# Assuming 'df' is your DataFrame
cols_to_drop = [col for col in Final_Arima_Results1.columns if col == "Year"][1:]
Final_Arima_Results1.drop(columns=cols_to_drop, inplace=True)

Final_Arima_Results1

Performing stepwise search to minimize aic
 ARIMA(2,1,2)(0,0,0)[0] intercept   : AIC=inf, Time=0.07 sec
 ARIMA(0,1,0)(0,0,0)[0] intercept   : AIC=236.689, Time=0.01 sec
 ARIMA(1,1,0)(0,0,0)[0] intercept   : AIC=235.659, Time=0.03 sec
 ARIMA(0,1,1)(0,0,0)[0] intercept   : AIC=236.015, Time=0.03 sec
 ARIMA(0,1,0)(0,0,0)[0]             : AIC=245.245, Time=0.01 sec
 ARIMA(2,1,0)(0,0,0)[0] intercept   : AIC=237.132, Time=0.04 sec
 ARIMA(1,1,1)(0,0,0)[0] intercept   : AIC=237.491, Time=0.06 sec
 ARIMA(2,1,1)(0,0,0)[0] intercept   : AIC=inf, Time=0.06 sec
 ARIMA(1,1,0)(0,0,0)[0]             : AIC=236.340, Time=0.02 sec

Best model:  ARIMA(1,1,0)(0,0,0)[0] intercept
Total fit time: 0.330 seconds

Performing stepwise search to minimize aic
 ARIMA(2,1,2)(0,0,0)[0] intercept   : AIC=340.697, Time=0.04 sec
 ARIMA(0,1,0)(0,0,0)[0] intercept   : AIC=353.277, Time=0.00 sec
 ARIMA(1,1,0)(0,0,0)[0] intercept   : AIC=346.962, Time=0.03 sec
 ARIMA(0,1,1)(0,0,0)[0] intercept   : AIC=347.049, Time=0.05 sec
 ARIMA(0,1,0)(0,0,0)[0]             : AIC=369.994, Time=0.01 sec
 ARIMA(1,1,2)(0,0,0)[0] intercept   : AIC=341.373, Time=0.04 sec
 ARIMA(2,1,1)(0,0,0)[0] intercept   : AIC=340.023, Time=0.03 sec
 ARIMA(1,1,1)(0,0,0)[0] intercept   : AIC=340.000, Time=0.02 sec
 ARIMA(0,1,2)(0,0,0)[0] intercept   : AIC=349.580, Time=0.02 sec
 ARIMA(2,1,0)(0,0,0)[0] intercept   : AIC=341.371, Time=0.01 sec
 ARIMA(1,1,1)(0,0,0)[0]             : AIC=339.532, Time=0.02 sec
 ARIMA(0,1,1)(0,0,0)[0]             : AIC=363.554, Time=0.01 sec
 ARIMA(1,1,0)(0,0,0)[0]             : AIC=366.773, Time=0.01 sec
 ARIMA(2,1,1)(0,0,0)[0]             : AIC=341.132, Time=0.03 sec
 ARIMA(1,1,2)(0,0,0)[0]             : AIC=340.978, Time=0.04 sec
 ARIMA(0,1,2)(0,0,0)[0]             : AIC=359.841, Time=0.01 sec
 ARIMA(2,1,0)(0,0,0)[0]             : AIC=351.243, Time=0.01 sec
 ARIMA(2,1,2)(0,0,0)[0]             : AIC=340.287, Time=0.09 sec

Best model:  ARIMA(1,1,1)(0,0,0)[0]          
Total fit time: 0.482 seconds

Performing stepwise search to minimize aic
 ARIMA(2,1,2)(0,0,0)[0] intercept   : AIC=inf, Time=0.10 sec
 ARIMA(0,1,0)(0,0,0)[0] intercept   : AIC=234.133, Time=0.01 sec
 ARIMA(1,1,0)(0,0,0)[0] intercept   : AIC=235.097, Time=0.02 sec
 ARIMA(0,1,1)(0,0,0)[0] intercept   : AIC=234.723, Time=0.03 sec
 ARIMA(0,1,0)(0,0,0)[0]             : AIC=246.012, Time=0.01 sec
 ARIMA(1,1,1)(0,0,0)[0] intercept   : AIC=236.664, Time=0.05 sec

Best model:  ARIMA(0,1,0)(0,0,0)[0] intercept
Total fit time: 0.208 seconds

Performing stepwise search to minimize aic
 ARIMA(2,1,2)(0,0,0)[0] intercept   : AIC=inf, Time=0.09 sec
 ARIMA(0,1,0)(0,0,0)[0] intercept   : AIC=250.867, Time=0.01 sec
 ARIMA(1,1,0)(0,0,0)[0] intercept   : AIC=251.893, Time=0.03 sec
 ARIMA(0,1,1)(0,0,0)[0] intercept   : AIC=251.647, Time=0.04 sec
 ARIMA(0,1,0)(0,0,0)[0]             : AIC=268.000, Time=0.01 sec
 ARIMA(1,1,1)(0,0,0)[0] intercept   : AIC=253.642, Time=0.04 sec

Best model:  ARIMA(0,1,0)(0,0,0)[0] intercept
Total fit time: 0.213 seconds
Out[12]:
year Total Energy Consumption, Agriculture, Forestry, Animal Husbandry and Fishery(10000 tons of SCE) Total Energy Consumption, Manufacturing(10000 tons of SCE) Total Energy Consumption, Manufacture of Computers, Communication and Other Electronic Equipment(10000 tons of SCE) Total Energy Consumption, Wholesale, Retail Trade and Hotel, Restaurants(10000 tons of SCE)
0 2003 5683.210000 111222.870000 1098.680000 4723.400000
1 2004 6391.860000 136407.850000 1316.210000 5498.790000
2 2005 6860.460000 158234.920000 1517.970000 5916.630000
3 2006 7153.520000 174920.090000 1886.510000 6358.180000
4 2007 7068.450000 193133.070000 2096.360000 6731.960000
5 2008 6872.630000 198406.180000 2219.950000 6884.910000
6 2009 6978.210000 206555.600000 2228.680000 7303.220000
7 2010 7266.500000 217328.870000 2547.390000 7847.100000
8 2011 7675.230000 229090.990000 2650.410000 9147.500000
9 2012 7803.570000 234538.810000 2689.440000 10012.330000
10 2013 8054.800000 239053.400000 2801.590000 10598.160000
11 2014 8020.000000 248976.000000 2968.000000 10864.000000
12 2015 8271.000000 248264.000000 3149.000000 11447.000000
13 2016 8585.000000 247658.000000 3377.000000 12042.000000
14 2017 8945.000000 252462.000000 3662.000000 12456.000000
15 2018 8781.000000 258604.000000 4628.000000 12994.000000
16 2019 9018.000000 268426.000000 5028.000000 13624.000000
17 2020 9263.000000 279651.000000 5120.000000 13171.000000
18 2021 9490.430575 283844.625975 5356.548235 13667.917647
19 2022 9710.376335 287419.303954 5593.096471 14164.835294
20 2023 9927.133462 290466.386055 5829.644706 14661.752941
21 2024 10142.532189 293063.741505 6066.192941 15158.670588
22 2025 10357.352217 295277.746614 6302.741176 15655.588235
23 2026 10571.925713 297164.981038 6539.289412 16152.505882
24 2027 10786.394183 298773.673701 6775.837647 16649.423529
25 2028 11000.817910 300144.935294 7012.385882 17146.341176
26 2029 11215.222576 301313.808877 7248.934118 17643.258824
27 2030 11429.619121 302310.165414 7485.482353 18140.176471
In [13]:
from pmdarima import auto_arima

cols = ["Total Energy Consumption(10000 tons of SCE)",
        "Proportion of Coal(%)",
        "Proportion of Petroleum(%)", 
        "Proportion of Natural Gas(%)",
        "Proportion of Primary  Electricity and  Other Energy(%)",
        "Consumption of Coal(10000 tons)", 
        "Consumption of Coke(10000 tons)",
        "Consumption of Crude Oil(10000 tons)",
        "Consumption of Gasoline(10000 tons)",
        "Consumption of Kerosene(10000 tons)",
        "Consumption of Diesel Oil(10000 tons)",
        "Consumption of Fuel Oil(10000 tons)",
        "Consumption of Natural Gas(100 million cu.m)",
        "Consumption of Electricity(100 million kwh)"]

Arima_Results = []

for col in cols:

    Target_Column = col

    df = pd.read_excel("D:\Jupyter Directory\Dissertation\Dissertation Datasets\Ones I will use\Annual Total Energy Consumption.xls")

    df.set_index("Database：Annual",inplace = True)

    new_df = df.T[["Indicators",Target_Column]].reset_index(drop = True)

    new_df = new_df.rename(columns={"Indicators": "Year"}).sort_values(by="Year")
    new_df["Year"] = new_df["Year"].astype("int")

    new_df.dropna(inplace = True)

    # Generate synthetic time series data
    t = new_df["Year"]
    y = new_df[Target_Column]

    # Find the best ARIMA model using auto_arima
    stepwise_fit = auto_arima(y, seasonal=False, trace=True)

    # Fit the best ARIMA model
    best_order = stepwise_fit.get_params()["order"]
    model = stepwise_fit.fit(y)

    # Forecast the next 10 data points
    forecast, conf_int = model.predict(n_periods=10, return_conf_int=True)

    # Append the forecasted values to the original data
    extended_t = np.concatenate((t, np.arange(t.iloc[-1]+1, t.iloc[-1]+11)))
    extended_y = np.concatenate((y, forecast))

    Result = pd.DataFrame({"Year":extended_t,Target_Column:extended_y})
    Arima_Results.append(Result)

    # Plot original data and forecasted values
    plt.figure(figsize=(10, 6))

    # Plot original data in a solid line
    plt.plot(t, y, color="dodgerblue", linewidth=2, label="Original Data")

    # Plot forecasted values as a dashed line with shaded confidence interval
    plt.plot(extended_t[-10:], forecast, linestyle="dashed", color="darkorange", label="Forecasted Values")
    plt.fill_between(extended_t[-10:], conf_int[:, 0], conf_int[:, 1], color="darkorange", alpha=0.1)

    # Set labels and title
    plt.xlabel("Year")
    plt.ylabel(Target_Column)
    plt.title(f"ARIMA Forecast (Order: {best_order})")

    # Add grid lines
    plt.grid(True)

    # Add legend
    plt.legend()

    # Show the plot
    plt.show()
    


Final_Arima_Results2 = pd.concat(Arima_Results, axis=1)

Final_Arima_Results2.columns.values[0] = "year"

# Assuming 'df' is your DataFrame
cols_to_drop = [col for col in Final_Arima_Results2.columns if col == "Year"][1:]
Final_Arima_Results2.drop(columns=cols_to_drop, inplace=True)

Final_Arima_Results2

Performing stepwise search to minimize aic
 ARIMA(2,1,2)(0,0,0)[0] intercept   : AIC=inf, Time=0.09 sec
 ARIMA(0,1,0)(0,0,0)[0] intercept   : AIC=398.490, Time=0.01 sec
 ARIMA(1,1,0)(0,0,0)[0] intercept   : AIC=400.361, Time=0.04 sec
 ARIMA(0,1,1)(0,0,0)[0] intercept   : AIC=397.281, Time=0.02 sec
 ARIMA(0,1,0)(0,0,0)[0]             : AIC=431.725, Time=0.01 sec
 ARIMA(1,1,1)(0,0,0)[0] intercept   : AIC=393.543, Time=0.03 sec
 ARIMA(2,1,1)(0,0,0)[0] intercept   : AIC=392.729, Time=0.02 sec
 ARIMA(2,1,0)(0,0,0)[0] intercept   : AIC=394.462, Time=0.02 sec
 ARIMA(3,1,1)(0,0,0)[0] intercept   : AIC=394.091, Time=0.03 sec
 ARIMA(1,1,2)(0,0,0)[0] intercept   : AIC=392.857, Time=0.03 sec
 ARIMA(3,1,0)(0,0,0)[0] intercept   : AIC=392.635, Time=0.02 sec
 ARIMA(4,1,0)(0,0,0)[0] intercept   : AIC=394.899, Time=0.02 sec
 ARIMA(4,1,1)(0,0,0)[0] intercept   : AIC=393.930, Time=0.04 sec
 ARIMA(3,1,0)(0,0,0)[0]             : AIC=421.137, Time=0.02 sec

Best model:  ARIMA(3,1,0)(0,0,0)[0] intercept
Total fit time: 0.396 seconds

Performing stepwise search to minimize aic
 ARIMA(2,1,2)(0,0,0)[0] intercept   : AIC=inf, Time=0.09 sec
 ARIMA(0,1,0)(0,0,0)[0] intercept   : AIC=62.219, Time=0.01 sec
 ARIMA(1,1,0)(0,0,0)[0] intercept   : AIC=63.854, Time=0.01 sec
 ARIMA(0,1,1)(0,0,0)[0] intercept   : AIC=64.030, Time=0.02 sec
 ARIMA(0,1,0)(0,0,0)[0]             : AIC=67.054, Time=0.01 sec
 ARIMA(1,1,1)(0,0,0)[0] intercept   : AIC=64.295, Time=0.03 sec

Best model:  ARIMA(0,1,0)(0,0,0)[0] intercept
Total fit time: 0.166 seconds

Performing stepwise search to minimize aic
 ARIMA(2,0,2)(0,0,0)[0]             : AIC=54.025, Time=0.06 sec
 ARIMA(0,0,0)(0,0,0)[0]             : AIC=165.843, Time=0.01 sec
 ARIMA(1,0,0)(0,0,0)[0]             : AIC=inf, Time=0.01 sec
 ARIMA(0,0,1)(0,0,0)[0]             : AIC=inf, Time=0.02 sec
 ARIMA(1,0,2)(0,0,0)[0]             : AIC=51.137, Time=0.04 sec
 ARIMA(0,0,2)(0,0,0)[0]             : AIC=inf, Time=0.04 sec
 ARIMA(1,0,1)(0,0,0)[0]             : AIC=50.766, Time=0.03 sec
 ARIMA(2,0,1)(0,0,0)[0]             : AIC=52.977, Time=0.06 sec
 ARIMA(2,0,0)(0,0,0)[0]             : AIC=inf, Time=0.02 sec
 ARIMA(1,0,1)(0,0,0)[0] intercept   : AIC=45.226, Time=0.04 sec
 ARIMA(0,0,1)(0,0,0)[0] intercept   : AIC=50.487, Time=0.01 sec
 ARIMA(1,0,0)(0,0,0)[0] intercept   : AIC=43.868, Time=0.04 sec
 ARIMA(0,0,0)(0,0,0)[0] intercept   : AIC=60.547, Time=0.00 sec
 ARIMA(2,0,0)(0,0,0)[0] intercept   : AIC=44.762, Time=0.05 sec
 ARIMA(2,0,1)(0,0,0)[0] intercept   : AIC=42.595, Time=0.08 sec
 ARIMA(3,0,1)(0,0,0)[0] intercept   : AIC=42.867, Time=0.11 sec
 ARIMA(2,0,2)(0,0,0)[0] intercept   : AIC=47.985, Time=0.09 sec
 ARIMA(1,0,2)(0,0,0)[0] intercept   : AIC=45.974, Time=0.05 sec
 ARIMA(3,0,0)(0,0,0)[0] intercept   : AIC=43.750, Time=0.08 sec
 ARIMA(3,0,2)(0,0,0)[0] intercept   : AIC=45.851, Time=0.11 sec

Best model:  ARIMA(2,0,1)(0,0,0)[0] intercept
Total fit time: 0.968 seconds

Performing stepwise search to minimize aic
 ARIMA(2,1,2)(0,0,0)[0] intercept   : AIC=3.594, Time=0.05 sec
 ARIMA(0,1,0)(0,0,0)[0] intercept   : AIC=-1.885, Time=0.01 sec
 ARIMA(1,1,0)(0,0,0)[0] intercept   : AIC=-1.243, Time=0.02 sec
 ARIMA(0,1,1)(0,0,0)[0] intercept   : AIC=-1.804, Time=0.02 sec
 ARIMA(0,1,0)(0,0,0)[0]             : AIC=21.879, Time=0.01 sec
 ARIMA(1,1,1)(0,0,0)[0] intercept   : AIC=-0.305, Time=0.04 sec

Best model:  ARIMA(0,1,0)(0,0,0)[0] intercept
Total fit time: 0.147 seconds

Performing stepwise search to minimize aic
 ARIMA(2,1,2)(0,0,0)[0] intercept   : AIC=35.646, Time=0.05 sec
 ARIMA(0,1,0)(0,0,0)[0] intercept   : AIC=33.134, Time=0.01 sec
 ARIMA(1,1,0)(0,0,0)[0] intercept   : AIC=34.448, Time=0.02 sec
 ARIMA(0,1,1)(0,0,0)[0] intercept   : AIC=34.770, Time=0.02 sec
 ARIMA(0,1,0)(0,0,0)[0]             : AIC=42.538, Time=0.01 sec
 ARIMA(1,1,1)(0,0,0)[0] intercept   : AIC=34.154, Time=0.04 sec

Best model:  ARIMA(0,1,0)(0,0,0)[0] intercept
Total fit time: 0.151 seconds

Performing stepwise search to minimize aic
 ARIMA(2,1,2)(0,0,0)[0] intercept   : AIC=inf, Time=0.09 sec
 ARIMA(0,1,0)(0,0,0)[0] intercept   : AIC=380.324, Time=0.01 sec
 ARIMA(1,1,0)(0,0,0)[0] intercept   : AIC=388.365, Time=0.01 sec
 ARIMA(0,1,1)(0,0,0)[0] intercept   : AIC=379.255, Time=0.01 sec
 ARIMA(0,1,0)(0,0,0)[0]             : AIC=387.402, Time=0.01 sec
 ARIMA(1,1,1)(0,0,0)[0] intercept   : AIC=376.012, Time=0.04 sec
 ARIMA(2,1,1)(0,0,0)[0] intercept   : AIC=380.458, Time=0.02 sec
 ARIMA(1,1,2)(0,0,0)[0] intercept   : AIC=378.612, Time=0.07 sec
 ARIMA(0,1,2)(0,0,0)[0] intercept   : AIC=377.684, Time=0.05 sec
 ARIMA(2,1,0)(0,0,0)[0] intercept   : AIC=380.441, Time=0.02 sec
 ARIMA(1,1,1)(0,0,0)[0]             : AIC=372.715, Time=0.03 sec
 ARIMA(0,1,1)(0,0,0)[0]             : AIC=383.295, Time=0.01 sec
 ARIMA(1,1,0)(0,0,0)[0]             : AIC=389.508, Time=0.01 sec
 ARIMA(2,1,1)(0,0,0)[0]             : AIC=375.191, Time=0.04 sec
 ARIMA(1,1,2)(0,0,0)[0]             : AIC=389.282, Time=0.03 sec
 ARIMA(0,1,2)(0,0,0)[0]             : AIC=389.545, Time=0.02 sec
 ARIMA(2,1,0)(0,0,0)[0]             : AIC=380.939, Time=0.01 sec
 ARIMA(2,1,2)(0,0,0)[0]             : AIC=inf, Time=0.08 sec

Best model:  ARIMA(1,1,1)(0,0,0)[0]          
Total fit time: 0.548 seconds

Performing stepwise search to minimize aic
 ARIMA(2,1,2)(0,0,0)[0] intercept   : AIC=304.225, Time=0.05 sec
 ARIMA(0,1,0)(0,0,0)[0] intercept   : AIC=313.431, Time=0.01 sec
 ARIMA(1,1,0)(0,0,0)[0] intercept   : AIC=314.887, Time=0.01 sec
 ARIMA(0,1,1)(0,0,0)[0] intercept   : AIC=316.410, Time=0.01 sec
 ARIMA(0,1,0)(0,0,0)[0]             : AIC=321.151, Time=0.01 sec
 ARIMA(1,1,2)(0,0,0)[0] intercept   : AIC=303.768, Time=0.04 sec
 ARIMA(0,1,2)(0,0,0)[0] intercept   : AIC=inf, Time=0.06 sec
 ARIMA(1,1,1)(0,0,0)[0] intercept   : AIC=313.863, Time=0.04 sec
 ARIMA(1,1,3)(0,0,0)[0] intercept   : AIC=inf, Time=0.08 sec
 ARIMA(0,1,3)(0,0,0)[0] intercept   : AIC=inf, Time=0.06 sec
 ARIMA(2,1,1)(0,0,0)[0] intercept   : AIC=309.212, Time=0.02 sec
 ARIMA(2,1,3)(0,0,0)[0] intercept   : AIC=inf, Time=0.12 sec
 ARIMA(1,1,2)(0,0,0)[0]             : AIC=inf, Time=0.05 sec

Best model:  ARIMA(1,1,2)(0,0,0)[0] intercept
Total fit time: 0.545 seconds

Performing stepwise search to minimize aic
 ARIMA(2,1,2)(0,0,0)[0] intercept   : AIC=292.441, Time=0.08 sec
 ARIMA(0,1,0)(0,0,0)[0] intercept   : AIC=287.882, Time=0.01 sec
 ARIMA(1,1,0)(0,0,0)[0] intercept   : AIC=291.635, Time=0.01 sec
 ARIMA(0,1,1)(0,0,0)[0] intercept   : AIC=288.699, Time=0.03 sec
 ARIMA(0,1,0)(0,0,0)[0]             : AIC=320.106, Time=0.01 sec
 ARIMA(1,1,1)(0,0,0)[0] intercept   : AIC=289.446, Time=0.02 sec

Best model:  ARIMA(0,1,0)(0,0,0)[0] intercept
Total fit time: 0.144 seconds

Performing stepwise search to minimize aic
 ARIMA(2,1,2)(0,0,0)[0] intercept   : AIC=inf, Time=0.08 sec
 ARIMA(0,1,0)(0,0,0)[0] intercept   : AIC=263.128, Time=0.01 sec
 ARIMA(1,1,0)(0,0,0)[0] intercept   : AIC=265.021, Time=0.01 sec
 ARIMA(0,1,1)(0,0,0)[0] intercept   : AIC=265.009, Time=0.02 sec
 ARIMA(0,1,0)(0,0,0)[0]             : AIC=273.420, Time=0.01 sec
 ARIMA(1,1,1)(0,0,0)[0] intercept   : AIC=267.132, Time=0.03 sec

Best model:  ARIMA(0,1,0)(0,0,0)[0] intercept
Total fit time: 0.155 seconds

Performing stepwise search to minimize aic
 ARIMA(2,1,2)(0,0,0)[0] intercept   : AIC=inf, Time=0.09 sec
 ARIMA(0,1,0)(0,0,0)[0] intercept   : AIC=234.949, Time=0.01 sec
 ARIMA(1,1,0)(0,0,0)[0] intercept   : AIC=236.882, Time=0.02 sec
 ARIMA(0,1,1)(0,0,0)[0] intercept   : AIC=236.849, Time=0.02 sec
 ARIMA(0,1,0)(0,0,0)[0]             : AIC=239.142, Time=0.01 sec
 ARIMA(1,1,1)(0,0,0)[0] intercept   : AIC=238.826, Time=0.03 sec

Best model:  ARIMA(0,1,0)(0,0,0)[0] intercept
Total fit time: 0.170 seconds

Performing stepwise search to minimize aic
 ARIMA(2,2,2)(0,0,0)[0] intercept   : AIC=inf, Time=0.09 sec
 ARIMA(0,2,0)(0,0,0)[0] intercept   : AIC=258.111, Time=0.01 sec
 ARIMA(1,2,0)(0,0,0)[0] intercept   : AIC=258.365, Time=0.03 sec
 ARIMA(0,2,1)(0,0,0)[0] intercept   : AIC=inf, Time=0.04 sec
 ARIMA(0,2,0)(0,0,0)[0]             : AIC=256.791, Time=0.01 sec
 ARIMA(1,2,1)(0,0,0)[0] intercept   : AIC=inf, Time=0.06 sec

Best model:  ARIMA(0,2,0)(0,0,0)[0]          
Total fit time: 0.232 seconds

Performing stepwise search to minimize aic
 ARIMA(2,0,2)(0,0,0)[0]             : AIC=inf, Time=nan sec
 ARIMA(0,0,0)(0,0,0)[0]             : AIC=354.125, Time=0.01 sec
 ARIMA(1,0,0)(0,0,0)[0]             : AIC=inf, Time=0.02 sec
 ARIMA(0,0,1)(0,0,0)[0]             : AIC=inf, Time=0.02 sec
 ARIMA(1,0,1)(0,0,0)[0]             : AIC=282.725, Time=0.03 sec
 ARIMA(2,0,1)(0,0,0)[0]             : AIC=284.676, Time=0.05 sec
 ARIMA(1,0,2)(0,0,0)[0]             : AIC=284.162, Time=0.07 sec
 ARIMA(0,0,2)(0,0,0)[0]             : AIC=inf, Time=0.03 sec
 ARIMA(2,0,0)(0,0,0)[0]             : AIC=inf, Time=0.03 sec
 ARIMA(1,0,1)(0,0,0)[0] intercept   : AIC=278.587, Time=0.01 sec
 ARIMA(0,0,1)(0,0,0)[0] intercept   : AIC=278.833, Time=0.03 sec
 ARIMA(1,0,0)(0,0,0)[0] intercept   : AIC=276.551, Time=0.01 sec
 ARIMA(0,0,0)(0,0,0)[0] intercept   : AIC=285.993, Time=0.01 sec
 ARIMA(2,0,0)(0,0,0)[0] intercept   : AIC=278.376, Time=0.02 sec
 ARIMA(2,0,1)(0,0,0)[0] intercept   : AIC=279.929, Time=0.08 sec

Best model:  ARIMA(1,0,0)(0,0,0)[0] intercept
Total fit time: 0.484 seconds

Performing stepwise search to minimize aic
 ARIMA(2,2,2)(0,0,0)[0] intercept   : AIC=inf, Time=0.07 sec
 ARIMA(0,2,0)(0,0,0)[0] intercept   : AIC=192.787, Time=0.01 sec
 ARIMA(1,2,0)(0,0,0)[0] intercept   : AIC=194.695, Time=0.02 sec
 ARIMA(0,2,1)(0,0,0)[0] intercept   : AIC=inf, Time=0.03 sec
 ARIMA(0,2,0)(0,0,0)[0]             : AIC=191.180, Time=0.01 sec
 ARIMA(1,2,1)(0,0,0)[0] intercept   : AIC=inf, Time=0.04 sec

Best model:  ARIMA(0,2,0)(0,0,0)[0]          
Total fit time: 0.182 seconds

Performing stepwise search to minimize aic
 ARIMA(2,1,2)(0,0,0)[0] intercept   : AIC=inf, Time=0.08 sec
 ARIMA(0,1,0)(0,0,0)[0] intercept   : AIC=295.657, Time=0.01 sec
 ARIMA(1,1,0)(0,0,0)[0] intercept   : AIC=298.058, Time=0.01 sec
 ARIMA(0,1,1)(0,0,0)[0] intercept   : AIC=297.739, Time=0.01 sec
 ARIMA(0,1,0)(0,0,0)[0]             : AIC=329.393, Time=0.01 sec
 ARIMA(1,1,1)(0,0,0)[0] intercept   : AIC=299.553, Time=0.04 sec

Best model:  ARIMA(0,1,0)(0,0,0)[0] intercept
Total fit time: 0.156 seconds
Out[13]:
year Total Energy Consumption(10000 tons of SCE) Proportion of Coal(%) Proportion of Petroleum(%) Proportion of Natural Gas(%) Proportion of Primary Electricity and Other Energy(%) Consumption of Coal(10000 tons) Consumption of Coke(10000 tons) Consumption of Crude Oil(10000 tons) Consumption of Gasoline(10000 tons) Consumption of Kerosene(10000 tons) Consumption of Diesel Oil(10000 tons) Consumption of Fuel Oil(10000 tons) Consumption of Natural Gas(100 million cu.m) Consumption of Electricity(100 million kwh)
0 2003 197083.000000 70.200000 20.100000 2.300000 7.400000 183760.240000 15926.470000 25180.720000 4118.520000 921.61 8575.12 4330.340000 339.08 19031.600000
1 2004 230281.000000 70.200000 19.900000 2.300000 7.600000 212161.830000 18067.010000 29009.310000 4695.720000 1060.86 10206.92 4844.760000 396.72 21971.370000
2 2005 261369.000000 72.400000 17.800000 2.400000 7.400000 243375.440000 25105.840000 30088.940000 4854.910000 1076.84 10974.94 4244.160000 466.08 24940.320000
3 2006 286467.000000 72.400000 17.500000 2.700000 7.400000 270639.450000 28297.760000 32245.200000 5242.550000 1124.74 11729.09 4471.150000 573.32 28587.970000
4 2007 311442.000000 72.500000 17.000000 3.000000 7.500000 290410.120000 31168.120000 34031.600000 5519.090000 1243.72 12492.38 4157.490000 705.23 32711.810000
5 2008 320611.000000 71.500000 16.700000 3.400000 8.400000 300604.940000 32120.240000 35510.340000 6145.520000 1294.01 13544.94 3236.750000 812.94 34541.350000
6 2009 336126.000000 71.600000 16.400000 3.500000 8.500000 325002.930000 36349.970000 38128.590000 6172.690000 1450.49 13551.43 2828.800000 895.20 37032.140000
7 2010 360648.000000 69.200000 17.400000 4.000000 9.400000 349008.260000 38702.790000 42874.550000 6956.200000 1765.17 14699.00 3758.020000 1080.24 41934.490000
8 2011 387043.000000 70.200000 16.800000 4.600000 8.400000 388961.100000 42063.280000 43965.840000 7595.950000 1816.72 15635.10 3662.800000 1341.07 47000.880000
9 2012 402138.000000 68.500000 17.000000 4.800000 9.700000 411726.900000 44805.230000 46678.920000 8165.900000 1956.60 16966.04 3683.280000 1497.00 49762.640000
10 2013 416913.000000 67.400000 17.100000 5.300000 10.200000 424425.940000 45851.870000 48652.150000 9366.350000 2164.07 17150.65 3953.970000 1705.37 54203.410000
11 2014 428334.000000 65.800000 17.300000 5.600000 11.300000 413633.000000 46885.000000 51596.950000 9776.370000 2335.42 17165.29 4355.470000 1870.63 57829.690000
12 2015 434113.000000 63.800000 18.400000 5.800000 12.000000 399834.000000 44059.000000 54788.280000 11368.460000 2663.71 17360.31 4662.010000 1931.75 58019.980000
13 2016 441492.000000 62.200000 18.700000 6.100000 13.000000 388820.000000 45462.000000 57125.930000 11866.040000 2970.71 16839.04 4631.040000 2078.06 61205.090000
14 2017 455827.000000 60.600000 18.900000 6.900000 13.600000 391403.000000 43743.000000 59402.170000 12296.270000 3326.36 16916.54 4887.300000 2393.69 65913.970000
15 2018 471925.000000 59.000000 18.900000 7.600000 14.500000 397452.000000 43717.000000 63004.330000 13055.300000 3653.51 16409.56 4536.070000 2817.09 71508.200000
16 2019 487488.000000 57.700000 19.000000 8.000000 15.300000 401915.000000 46426.000000 67268.270000 13627.970000 3950.23 14917.95 4690.340000 3059.68 74866.120000
17 2020 498314.000000 56.900000 18.800000 8.400000 15.900000 404860.000000 48310.000000 69477.140000 12767.160000 3352.10 14282.70 5364.600000 3339.89 77620.170000
18 2021 524000.000000 56.000000 18.500000 8.900000 16.600000 406491.730277 50528.015420 72082.811765 13275.903529 3495.07 13647.45 5157.572502 3620.10 81066.556471
19 2022 541000.000000 56.200000 17.952838 9.266666 17.111111 407893.569320 53335.089824 74688.483529 13784.647059 3638.04 13012.20 4994.840721 3900.31 84512.942941
20 2023 556580.025388 55.463158 17.406739 9.633333 17.622222 409097.906049 55215.970001 77294.155294 14293.390588 3781.01 12376.95 4866.927121 4180.52 87959.329412
21 2024 571988.562932 54.726316 16.965344 9.999999 18.133333 410132.566172 56755.741081 79899.827059 14802.134118 3923.98 11741.70 4766.381985 4460.73 91405.715882
22 2025 586886.656961 53.989474 16.709009 10.366665 18.644444 411021.455088 58169.884715 82505.498824 15310.877647 4066.95 11106.45 4687.349541 4740.94 94852.102353
23 2026 601658.190647 53.252632 16.680309 10.733331 19.155555 411785.110212 59537.760877 85111.170588 15819.621176 4209.92 10471.20 4625.226922 5021.15 98298.488824
24 2027 616369.458281 52.515789 16.877203 11.099998 19.666667 412441.175485 60888.597142 87716.842353 16328.364706 4352.89 9835.95 4576.396093 5301.36 101744.875294
25 2028 631042.354450 51.778947 17.254885 11.466664 20.177778 413004.809026 62233.157763 90322.514118 16837.108235 4495.86 9200.70 4538.013134 5581.57 105191.261765
26 2029 645701.137935 51.042105 17.735703 11.833330 20.688889 413489.033359 63575.407121 92928.185882 17345.851765 4638.83 8565.45 4507.842615 5861.78 108637.648235
27 2030 660352.852806 50.305263 18.224979 12.199996 21.200000 413905.036289 64916.805262 95533.857647 17854.595294 4781.80 7930.20 4484.127396 6141.99 112084.034706
28 2031 675001.069343 49.568421 18.629545 12.566663 21.711111 NaN NaN NaN NaN NaN NaN NaN NaN NaN
29 2032 689647.762238 48.831579 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN
In [14]:
from keras.models import Sequential
from keras.layers import LSTM, Dense

cols = ["Total Energy Consumption, Agriculture, Forestry, Animal Husbandry and Fishery(10000 tons of SCE)", 
        "Total Energy Consumption, Manufacturing(10000 tons of SCE)", 
        "Total Energy Consumption, Manufacture of Computers, Communication and Other Electronic Equipment(10000 tons of SCE)",
        "Total Energy Consumption, Wholesale, Retail Trade and Hotel, Restaurants(10000 tons of SCE)"]

LSTM_Results = []

for col in cols:

    # Load data
    target_column = col
    df = pd.read_excel("D:\Jupyter Directory\Dissertation\Dissertation Datasets\Ones I will use\Consumption by Sector.xls")

    # Set the index
    df.set_index("Database：Annual", inplace=True)

    # Prepare the data
    new_df = df.T[["Indicators", target_column]].reset_index(drop=True)
    new_df = new_df.rename(columns={"Indicators": "Year"}).sort_values(by="Year")
    new_df["Year"] = new_df["Year"].astype("int")
    new_df.dropna(inplace=True)

    # Select the target column
    y = new_df[target_column].values

    # Normalize the data
    scaler = MinMaxScaler()
    y_scaled = scaler.fit_transform(y.reshape(-1, 1))

    # Prepare the data for LSTM
    look_back = 5  # Number of previous time steps to use for prediction
    X = []
    y_lstm = []
    for i in range(len(y_scaled) - look_back):
        X.append(y_scaled[i:(i + look_back)])
        y_lstm.append(y_scaled[i + look_back])
    X, y_lstm = np.array(X), np.array(y_lstm)

    # Reshape the data for LSTM (samples, time steps, features)
    X = X.reshape(X.shape[0], X.shape[1], 1)

    # Build the LSTM model
    model = Sequential()
    model.add(LSTM(units=50, return_sequences=True, input_shape=(X.shape[1], 1)))
    model.add(LSTM(units=50, return_sequences=False))
    model.add(Dense(units=1))
    model.compile(optimizer="adam", loss="mean_squared_error")

    # Train the model
    model.fit(X, y_lstm, epochs=100, batch_size=32)

    # Forecast the next 10 data points
    last_sequence = y_scaled[-look_back:]
    forecast_scaled = []
    for _ in range(10):
        input_data = np.array([last_sequence[-look_back:]])
        input_data = input_data.reshape((1, look_back, 1))
        forecast_scaled.append(model.predict(input_data)[0, 0])
        last_sequence = np.append(last_sequence, forecast_scaled[-1])

    # Inverse transform the forecasted data
    forecast = scaler.inverse_transform(np.array(forecast_scaled).reshape(-1, 1)).flatten()

    # Append the forecasted values to the original data
    extended_t = np.concatenate((new_df["Year"], np.arange(new_df["Year"].iloc[-1] + 1, new_df["Year"].iloc[-1] + 11)))
    extended_y = np.concatenate((y, forecast))

    Result = pd.DataFrame({"Year": extended_t, target_column: extended_y})

    LSTM_Results.append(Result)

    # Plot original data and forecasted values
    plt.figure(figsize=(10, 6))
    plt.plot(new_df["Year"], y, color="dodgerblue", linewidth=2, label="Original Data")
    plt.plot(extended_t[-10:], forecast, linestyle="dashed", color="darkorange", label="Forecasted Values")
    plt.xlabel("Year")
    plt.ylabel(target_column)
    plt.title("LSTM Forecast")
    plt.grid(True)
    plt.legend()
    plt.show()

Final_LSTM_Results1 = pd.concat(LSTM_Results, axis=1)

Final_LSTM_Results1.columns.values[0] = "year"

# Assuming 'df' is your DataFrame
cols_to_drop = [col for col in Final_LSTM_Results1.columns if col == "Year"][1:]
Final_LSTM_Results1.drop(columns=cols_to_drop, inplace=True)

Final_LSTM_Results1

Epoch 1/100
1/1 [==============================] - 2s 2s/step - loss: 0.5454
Epoch 2/100
1/1 [==============================] - 0s 3ms/step - loss: 0.5088
Epoch 3/100
1/1 [==============================] - 0s 3ms/step - loss: 0.4738
Epoch 4/100
1/1 [==============================] - 0s 3ms/step - loss: 0.4398
Epoch 5/100
1/1 [==============================] - 0s 5ms/step - loss: 0.4064
Epoch 6/100
1/1 [==============================] - 0s 4ms/step - loss: 0.3734
Epoch 7/100
1/1 [==============================] - 0s 3ms/step - loss: 0.3407
Epoch 8/100
1/1 [==============================] - 0s 4ms/step - loss: 0.3079
Epoch 9/100
1/1 [==============================] - 0s 4ms/step - loss: 0.2752
Epoch 10/100
1/1 [==============================] - 0s 3ms/step - loss: 0.2425
Epoch 11/100
1/1 [==============================] - 0s 4ms/step - loss: 0.2098
Epoch 12/100
1/1 [==============================] - 0s 5ms/step - loss: 0.1775
Epoch 13/100
1/1 [==============================] - 0s 4ms/step - loss: 0.1459
Epoch 14/100
1/1 [==============================] - 0s 5ms/step - loss: 0.1153
Epoch 15/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0866
Epoch 16/100
1/1 [==============================] - 0s 3ms/step - loss: 0.0604
Epoch 17/100
1/1 [==============================] - 0s 5ms/step - loss: 0.0381
Epoch 18/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0207
Epoch 19/100
1/1 [==============================] - 0s 3ms/step - loss: 0.0098
Epoch 20/100
1/1 [==============================] - 0s 3ms/step - loss: 0.0065
Epoch 21/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0110
Epoch 22/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0214
Epoch 23/100
1/1 [==============================] - 0s 5ms/step - loss: 0.0334
Epoch 24/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0421
Epoch 25/100
1/1 [==============================] - 0s 5ms/step - loss: 0.0448
Epoch 26/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0418
Epoch 27/100
1/1 [==============================] - 0s 5ms/step - loss: 0.0350
Epoch 28/100
1/1 [==============================] - 0s 5ms/step - loss: 0.0267
Epoch 29/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0188
Epoch 30/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0127
Epoch 31/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0087
Epoch 32/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0068
Epoch 33/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0066
Epoch 34/100
1/1 [==============================] - 0s 5ms/step - loss: 0.0075
Epoch 35/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0091
Epoch 36/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0108
Epoch 37/100
1/1 [==============================] - 0s 5ms/step - loss: 0.0124
Epoch 38/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0135
Epoch 39/100
1/1 [==============================] - 0s 5ms/step - loss: 0.0142
Epoch 40/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0142
Epoch 41/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0138
Epoch 42/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0129
Epoch 43/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0117
Epoch 44/100
1/1 [==============================] - 0s 5ms/step - loss: 0.0104
Epoch 45/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0090
Epoch 46/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0078
Epoch 47/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0069
Epoch 48/100
1/1 [==============================] - 0s 5ms/step - loss: 0.0063
Epoch 49/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0060
Epoch 50/100
1/1 [==============================] - 0s 3ms/step - loss: 0.0060
Epoch 51/100
1/1 [==============================] - 0s 5ms/step - loss: 0.0063
Epoch 52/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0066
Epoch 53/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0070
Epoch 54/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0073
Epoch 55/100
1/1 [==============================] - 0s 5ms/step - loss: 0.0074
Epoch 56/100
1/1 [==============================] - 0s 3ms/step - loss: 0.0072
Epoch 57/100
1/1 [==============================] - 0s 5ms/step - loss: 0.0070
Epoch 58/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0066
Epoch 59/100
1/1 [==============================] - 0s 3ms/step - loss: 0.0062
Epoch 60/100
1/1 [==============================] - 0s 3ms/step - loss: 0.0058
Epoch 61/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0055
Epoch 62/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0053
Epoch 63/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0052
Epoch 64/100
1/1 [==============================] - 0s 3ms/step - loss: 0.0052
Epoch 65/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0052
Epoch 66/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0053
Epoch 67/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0053
Epoch 68/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0053
Epoch 69/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0053
Epoch 70/100
1/1 [==============================] - 0s 5ms/step - loss: 0.0052
Epoch 71/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0051
Epoch 72/100
1/1 [==============================] - 0s 3ms/step - loss: 0.0049
Epoch 73/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0048
Epoch 74/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0047
Epoch 75/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0046
Epoch 76/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0045
Epoch 77/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0044
Epoch 78/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0044
Epoch 79/100
1/1 [==============================] - 0s 5ms/step - loss: 0.0044
Epoch 80/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0044
Epoch 81/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0044
Epoch 82/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0043
Epoch 83/100
1/1 [==============================] - 0s 3ms/step - loss: 0.0043
Epoch 84/100
1/1 [==============================] - 0s 5ms/step - loss: 0.0042
Epoch 85/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0041
Epoch 86/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0040
Epoch 87/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0040
Epoch 88/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0039
Epoch 89/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0039
Epoch 90/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0039
Epoch 91/100
1/1 [==============================] - 0s 5ms/step - loss: 0.0038
Epoch 92/100
1/1 [==============================] - 0s 5ms/step - loss: 0.0038
Epoch 93/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0038
Epoch 94/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0037
Epoch 95/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0037
Epoch 96/100
1/1 [==============================] - 0s 5ms/step - loss: 0.0037
Epoch 97/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0036
Epoch 98/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0036
Epoch 99/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0035
Epoch 100/100
1/1 [==============================] - 0s 5ms/step - loss: 0.0035
1/1 [==============================] - 0s 463ms/step
1/1 [==============================] - 0s 11ms/step
1/1 [==============================] - 0s 11ms/step
1/1 [==============================] - 0s 12ms/step
1/1 [==============================] - 0s 13ms/step
1/1 [==============================] - 0s 11ms/step
1/1 [==============================] - 0s 11ms/step
1/1 [==============================] - 0s 12ms/step
1/1 [==============================] - 0s 12ms/step
1/1 [==============================] - 0s 11ms/step

Epoch 1/100
1/1 [==============================] - 2s 2s/step - loss: 0.5454
Epoch 2/100
1/1 [==============================] - 0s 4ms/step - loss: 0.5009
Epoch 3/100
1/1 [==============================] - 0s 3ms/step - loss: 0.4572
Epoch 4/100
1/1 [==============================] - 0s 4ms/step - loss: 0.4141
Epoch 5/100
1/1 [==============================] - 0s 4ms/step - loss: 0.3716
Epoch 6/100
1/1 [==============================] - 0s 4ms/step - loss: 0.3296
Epoch 7/100
1/1 [==============================] - 0s 4ms/step - loss: 0.2881
Epoch 8/100
1/1 [==============================] - 0s 4ms/step - loss: 0.2473
Epoch 9/100
1/1 [==============================] - 0s 3ms/step - loss: 0.2072
Epoch 10/100
1/1 [==============================] - 0s 4ms/step - loss: 0.1683
Epoch 11/100
1/1 [==============================] - 0s 3ms/step - loss: 0.1312
Epoch 12/100
1/1 [==============================] - 0s 3ms/step - loss: 0.0965
Epoch 13/100
1/1 [==============================] - 0s 3ms/step - loss: 0.0653
Epoch 14/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0389
Epoch 15/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0188
Epoch 16/100
1/1 [==============================] - 0s 3ms/step - loss: 0.0065
Epoch 17/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0034
Epoch 18/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0093
Epoch 19/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0216
Epoch 20/100
1/1 [==============================] - 0s 3ms/step - loss: 0.0347
Epoch 21/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0435
Epoch 22/100
1/1 [==============================] - 0s 3ms/step - loss: 0.0455
Epoch 23/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0415
Epoch 24/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0336
Epoch 25/100
1/1 [==============================] - 0s 3ms/step - loss: 0.0243
Epoch 26/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0158
Epoch 27/100
1/1 [==============================] - 0s 5ms/step - loss: 0.0091
Epoch 28/100
1/1 [==============================] - 0s 3ms/step - loss: 0.0048
Epoch 29/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0028
Epoch 30/100
1/1 [==============================] - 0s 5ms/step - loss: 0.0026
Epoch 31/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0036
Epoch 32/100
1/1 [==============================] - 0s 3ms/step - loss: 0.0053
Epoch 33/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0071
Epoch 34/100
1/1 [==============================] - 0s 5ms/step - loss: 0.0087
Epoch 35/100
1/1 [==============================] - 0s 5ms/step - loss: 0.0099
Epoch 36/100
1/1 [==============================] - 0s 3ms/step - loss: 0.0106
Epoch 37/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0106
Epoch 38/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0101
Epoch 39/100
1/1 [==============================] - 0s 3ms/step - loss: 0.0092
Epoch 40/100
1/1 [==============================] - 0s 5ms/step - loss: 0.0080
Epoch 41/100
1/1 [==============================] - 0s 3ms/step - loss: 0.0066
Epoch 42/100
1/1 [==============================] - 0s 5ms/step - loss: 0.0052
Epoch 43/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0039
Epoch 44/100
1/1 [==============================] - 0s 5ms/step - loss: 0.0029
Epoch 45/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0023
Epoch 46/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0020
Epoch 47/100
1/1 [==============================] - 0s 3ms/step - loss: 0.0020
Epoch 48/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0022
Epoch 49/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0026
Epoch 50/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0030
Epoch 51/100
1/1 [==============================] - 0s 3ms/step - loss: 0.0034
Epoch 52/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0035
Epoch 53/100
1/1 [==============================] - 0s 5ms/step - loss: 0.0035
Epoch 54/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0033
Epoch 55/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0030
Epoch 56/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0027
Epoch 57/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0023
Epoch 58/100
1/1 [==============================] - 0s 5ms/step - loss: 0.0020
Epoch 59/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0018
Epoch 60/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0017
Epoch 61/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0017
Epoch 62/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0017
Epoch 63/100
1/1 [==============================] - 0s 3ms/step - loss: 0.0018
Epoch 64/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0019
Epoch 65/100
1/1 [==============================] - 0s 5ms/step - loss: 0.0020
Epoch 66/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0020
Epoch 67/100
1/1 [==============================] - 0s 5ms/step - loss: 0.0020
Epoch 68/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0020
Epoch 69/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0019
Epoch 70/100
1/1 [==============================] - 0s 3ms/step - loss: 0.0018
Epoch 71/100
1/1 [==============================] - 0s 3ms/step - loss: 0.0017
Epoch 72/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0016
Epoch 73/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0016
Epoch 74/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0015
Epoch 75/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0015
Epoch 76/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0015
Epoch 77/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0015
Epoch 78/100
1/1 [==============================] - 0s 3ms/step - loss: 0.0016
Epoch 79/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0016
Epoch 80/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0016
Epoch 81/100
1/1 [==============================] - 0s 3ms/step - loss: 0.0016
Epoch 82/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0015
Epoch 83/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0015
Epoch 84/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0015
Epoch 85/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0015
Epoch 86/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0014
Epoch 87/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0014
Epoch 88/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0014
Epoch 89/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0014
Epoch 90/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0014
Epoch 91/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0014
Epoch 92/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0014
Epoch 93/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0014
Epoch 94/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0014
Epoch 95/100
1/1 [==============================] - 0s 3ms/step - loss: 0.0014
Epoch 96/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0014
Epoch 97/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0014
Epoch 98/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0013
Epoch 99/100
1/1 [==============================] - 0s 3ms/step - loss: 0.0013
Epoch 100/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0013
1/1 [==============================] - 0s 447ms/step
1/1 [==============================] - 0s 11ms/step
1/1 [==============================] - 0s 11ms/step
1/1 [==============================] - 0s 11ms/step
1/1 [==============================] - 0s 11ms/step
1/1 [==============================] - 0s 12ms/step
1/1 [==============================] - 0s 13ms/step
1/1 [==============================] - 0s 12ms/step
1/1 [==============================] - 0s 12ms/step
1/1 [==============================] - 0s 12ms/step

Epoch 1/100
1/1 [==============================] - 3s 3s/step - loss: 0.3350
Epoch 2/100
1/1 [==============================] - 0s 4ms/step - loss: 0.3080
Epoch 3/100
1/1 [==============================] - 0s 4ms/step - loss: 0.2816
Epoch 4/100
1/1 [==============================] - 0s 4ms/step - loss: 0.2557
Epoch 5/100
1/1 [==============================] - 0s 4ms/step - loss: 0.2302
Epoch 6/100
1/1 [==============================] - 0s 4ms/step - loss: 0.2049
Epoch 7/100
1/1 [==============================] - 0s 5ms/step - loss: 0.1800
Epoch 8/100
1/1 [==============================] - 0s 3ms/step - loss: 0.1555
Epoch 9/100
1/1 [==============================] - 0s 4ms/step - loss: 0.1316
Epoch 10/100
1/1 [==============================] - 0s 4ms/step - loss: 0.1086
Epoch 11/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0867
Epoch 12/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0665
Epoch 13/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0485
Epoch 14/100
1/1 [==============================] - 0s 3ms/step - loss: 0.0335
Epoch 15/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0223
Epoch 16/100
1/1 [==============================] - 0s 5ms/step - loss: 0.0157
Epoch 17/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0143
Epoch 18/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0179
Epoch 19/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0249
Epoch 20/100
1/1 [==============================] - 0s 3ms/step - loss: 0.0325
Epoch 21/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0377
Epoch 22/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0391
Epoch 23/100
1/1 [==============================] - 0s 5ms/step - loss: 0.0368
Epoch 24/100
1/1 [==============================] - 0s 3ms/step - loss: 0.0322
Epoch 25/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0267
Epoch 26/100
1/1 [==============================] - 0s 3ms/step - loss: 0.0215
Epoch 27/100
1/1 [==============================] - 0s 5ms/step - loss: 0.0173
Epoch 28/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0146
Epoch 29/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0132
Epoch 30/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0130
Epoch 31/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0135
Epoch 32/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0144
Epoch 33/100
1/1 [==============================] - 0s 5ms/step - loss: 0.0155
Epoch 34/100
1/1 [==============================] - 0s 5ms/step - loss: 0.0164
Epoch 35/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0171
Epoch 36/100
1/1 [==============================] - 0s 5ms/step - loss: 0.0174
Epoch 37/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0173
Epoch 38/100
1/1 [==============================] - 0s 3ms/step - loss: 0.0168
Epoch 39/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0160
Epoch 40/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0150
Epoch 41/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0140
Epoch 42/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0129
Epoch 43/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0120
Epoch 44/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0113
Epoch 45/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0108
Epoch 46/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0106
Epoch 47/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0106
Epoch 48/100
1/1 [==============================] - 0s 5ms/step - loss: 0.0107
Epoch 49/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0108
Epoch 50/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0110
Epoch 51/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0110
Epoch 52/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0108
Epoch 53/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0105
Epoch 54/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0102
Epoch 55/100
1/1 [==============================] - 0s 5ms/step - loss: 0.0097
Epoch 56/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0093
Epoch 57/100
1/1 [==============================] - 0s 3ms/step - loss: 0.0090
Epoch 58/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0087
Epoch 59/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0085
Epoch 60/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0084
Epoch 61/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0083
Epoch 62/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0083
Epoch 63/100
1/1 [==============================] - 0s 5ms/step - loss: 0.0082
Epoch 64/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0081
Epoch 65/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0080
Epoch 66/100
1/1 [==============================] - 0s 5ms/step - loss: 0.0078
Epoch 67/100
1/1 [==============================] - 0s 3ms/step - loss: 0.0076
Epoch 68/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0074
Epoch 69/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0072
Epoch 70/100
1/1 [==============================] - 0s 3ms/step - loss: 0.0070
Epoch 71/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0069
Epoch 72/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0068
Epoch 73/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0067
Epoch 74/100
1/1 [==============================] - 0s 5ms/step - loss: 0.0066
Epoch 75/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0066
Epoch 76/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0065
Epoch 77/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0064
Epoch 78/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0063
Epoch 79/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0062
Epoch 80/100
1/1 [==============================] - 0s 5ms/step - loss: 0.0061
Epoch 81/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0060
Epoch 82/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0059
Epoch 83/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0058
Epoch 84/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0058
Epoch 85/100
1/1 [==============================] - 0s 3ms/step - loss: 0.0057
Epoch 86/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0057
Epoch 87/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0056
Epoch 88/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0056
Epoch 89/100
1/1 [==============================] - 0s 5ms/step - loss: 0.0055
Epoch 90/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0055
Epoch 91/100
1/1 [==============================] - 0s 3ms/step - loss: 0.0054
Epoch 92/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0054
Epoch 93/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0053
Epoch 94/100
1/1 [==============================] - 0s 3ms/step - loss: 0.0053
Epoch 95/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0052
Epoch 96/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0052
Epoch 97/100
1/1 [==============================] - 0s 3ms/step - loss: 0.0052
Epoch 98/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0052
Epoch 99/100
1/1 [==============================] - 0s 3ms/step - loss: 0.0052
Epoch 100/100
1/1 [==============================] - 0s 3ms/step - loss: 0.0051
1/1 [==============================] - 0s 432ms/step
1/1 [==============================] - 0s 11ms/step
1/1 [==============================] - 0s 12ms/step
1/1 [==============================] - 0s 12ms/step
1/1 [==============================] - 0s 12ms/step
1/1 [==============================] - 0s 11ms/step
1/1 [==============================] - 0s 11ms/step
1/1 [==============================] - 0s 11ms/step
1/1 [==============================] - 0s 12ms/step
1/1 [==============================] - 0s 11ms/step

Epoch 1/100
1/1 [==============================] - 2s 2s/step - loss: 0.4767
Epoch 2/100
1/1 [==============================] - 0s 4ms/step - loss: 0.4441
Epoch 3/100
1/1 [==============================] - 0s 3ms/step - loss: 0.4124
Epoch 4/100
1/1 [==============================] - 0s 4ms/step - loss: 0.3812
Epoch 5/100
1/1 [==============================] - 0s 4ms/step - loss: 0.3503
Epoch 6/100
1/1 [==============================] - 0s 3ms/step - loss: 0.3194
Epoch 7/100
1/1 [==============================] - 0s 3ms/step - loss: 0.2886
Epoch 8/100
1/1 [==============================] - 0s 4ms/step - loss: 0.2576
Epoch 9/100
1/1 [==============================] - 0s 3ms/step - loss: 0.2266
Epoch 10/100
1/1 [==============================] - 0s 3ms/step - loss: 0.1956
Epoch 11/100
1/1 [==============================] - 0s 4ms/step - loss: 0.1650
Epoch 12/100
1/1 [==============================] - 0s 3ms/step - loss: 0.1350
Epoch 13/100
1/1 [==============================] - 0s 3ms/step - loss: 0.1060
Epoch 14/100
1/1 [==============================] - 0s 3ms/step - loss: 0.0788
Epoch 15/100
1/1 [==============================] - 0s 3ms/step - loss: 0.0542
Epoch 16/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0333
Epoch 17/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0172
Epoch 18/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0074
Epoch 19/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0050
Epoch 20/100
1/1 [==============================] - 0s 3ms/step - loss: 0.0098
Epoch 21/100
1/1 [==============================] - 0s 3ms/step - loss: 0.0200
Epoch 22/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0311
Epoch 23/100
1/1 [==============================] - 0s 3ms/step - loss: 0.0387
Epoch 24/100
1/1 [==============================] - 0s 5ms/step - loss: 0.0406
Epoch 25/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0374
Epoch 26/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0308
Epoch 27/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0231
Epoch 28/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0159
Epoch 29/100
1/1 [==============================] - 0s 5ms/step - loss: 0.0103
Epoch 30/100
1/1 [==============================] - 0s 5ms/step - loss: 0.0068
Epoch 31/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0051
Epoch 32/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0050
Epoch 33/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0059
Epoch 34/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0074
Epoch 35/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0090
Epoch 36/100
1/1 [==============================] - 0s 3ms/step - loss: 0.0104
Epoch 37/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0115
Epoch 38/100
1/1 [==============================] - 0s 3ms/step - loss: 0.0120
Epoch 39/100
1/1 [==============================] - 0s 3ms/step - loss: 0.0121
Epoch 40/100
1/1 [==============================] - 0s 3ms/step - loss: 0.0116
Epoch 41/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0108
Epoch 42/100
1/1 [==============================] - 0s 3ms/step - loss: 0.0097
Epoch 43/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0085
Epoch 44/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0073
Epoch 45/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0063
Epoch 46/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0054
Epoch 47/100
1/1 [==============================] - 0s 3ms/step - loss: 0.0049
Epoch 48/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0047
Epoch 49/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0048
Epoch 50/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0051
Epoch 51/100
1/1 [==============================] - 0s 5ms/step - loss: 0.0054
Epoch 52/100
1/1 [==============================] - 0s 5ms/step - loss: 0.0058
Epoch 53/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0060
Epoch 54/100
1/1 [==============================] - 0s 3ms/step - loss: 0.0061
Epoch 55/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0061
Epoch 56/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0059
Epoch 57/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0056
Epoch 58/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0052
Epoch 59/100
1/1 [==============================] - 0s 3ms/step - loss: 0.0049
Epoch 60/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0047
Epoch 61/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0045
Epoch 62/100
1/1 [==============================] - 0s 5ms/step - loss: 0.0045
Epoch 63/100
1/1 [==============================] - 0s 3ms/step - loss: 0.0045
Epoch 64/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0045
Epoch 65/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0046
Epoch 66/100
1/1 [==============================] - 0s 5ms/step - loss: 0.0047
Epoch 67/100
1/1 [==============================] - 0s 3ms/step - loss: 0.0047
Epoch 68/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0047
Epoch 69/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0047
Epoch 70/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0047
Epoch 71/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0046
Epoch 72/100
1/1 [==============================] - 0s 8ms/step - loss: 0.0045
Epoch 73/100
1/1 [==============================] - 0s 7ms/step - loss: 0.0044
Epoch 74/100
1/1 [==============================] - 0s 7ms/step - loss: 0.0043
Epoch 75/100
1/1 [==============================] - 0s 6ms/step - loss: 0.0043
Epoch 76/100
1/1 [==============================] - 0s 5ms/step - loss: 0.0043
Epoch 77/100
1/1 [==============================] - 0s 5ms/step - loss: 0.0043
Epoch 78/100
1/1 [==============================] - 0s 6ms/step - loss: 0.0043
Epoch 79/100
1/1 [==============================] - 0s 6ms/step - loss: 0.0043
Epoch 80/100
1/1 [==============================] - 0s 5ms/step - loss: 0.0043
Epoch 81/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0043
Epoch 82/100
1/1 [==============================] - 0s 3ms/step - loss: 0.0043
Epoch 83/100
1/1 [==============================] - 0s 6ms/step - loss: 0.0043
Epoch 84/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0042
Epoch 85/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0042
Epoch 86/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0042
Epoch 87/100
1/1 [==============================] - 0s 3ms/step - loss: 0.0042
Epoch 88/100
1/1 [==============================] - 0s 3ms/step - loss: 0.0041
Epoch 89/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0041
Epoch 90/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0041
Epoch 91/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0041
Epoch 92/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0041
Epoch 93/100
1/1 [==============================] - 0s 3ms/step - loss: 0.0041
Epoch 94/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0041
Epoch 95/100
1/1 [==============================] - 0s 3ms/step - loss: 0.0041
Epoch 96/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0041
Epoch 97/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0041
Epoch 98/100
1/1 [==============================] - 0s 3ms/step - loss: 0.0041
Epoch 99/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0041
Epoch 100/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0041
1/1 [==============================] - 0s 432ms/step
1/1 [==============================] - 0s 12ms/step
1/1 [==============================] - 0s 12ms/step
1/1 [==============================] - 0s 11ms/step
1/1 [==============================] - 0s 12ms/step
1/1 [==============================] - 0s 12ms/step
1/1 [==============================] - 0s 12ms/step
1/1 [==============================] - 0s 12ms/step
1/1 [==============================] - 0s 11ms/step
1/1 [==============================] - 0s 12ms/step
Out[14]:
year Total Energy Consumption, Agriculture, Forestry, Animal Husbandry and Fishery(10000 tons of SCE) Total Energy Consumption, Manufacturing(10000 tons of SCE) Total Energy Consumption, Manufacture of Computers, Communication and Other Electronic Equipment(10000 tons of SCE) Total Energy Consumption, Wholesale, Retail Trade and Hotel, Restaurants(10000 tons of SCE)
0 2003 5683.210000 111222.87000 1098.680000 4723.400000
1 2004 6391.860000 136407.85000 1316.210000 5498.790000
2 2005 6860.460000 158234.92000 1517.970000 5916.630000
3 2006 7153.520000 174920.09000 1886.510000 6358.180000
4 2007 7068.450000 193133.07000 2096.360000 6731.960000
5 2008 6872.630000 198406.18000 2219.950000 6884.910000
6 2009 6978.210000 206555.60000 2228.680000 7303.220000
7 2010 7266.500000 217328.87000 2547.390000 7847.100000
8 2011 7675.230000 229090.99000 2650.410000 9147.500000
9 2012 7803.570000 234538.81000 2689.440000 10012.330000
10 2013 8054.800000 239053.40000 2801.590000 10598.160000
11 2014 8020.000000 248976.00000 2968.000000 10864.000000
12 2015 8271.000000 248264.00000 3149.000000 11447.000000
13 2016 8585.000000 247658.00000 3377.000000 12042.000000
14 2017 8945.000000 252462.00000 3662.000000 12456.000000
15 2018 8781.000000 258604.00000 4628.000000 12994.000000
16 2019 9018.000000 268426.00000 5028.000000 13624.000000
17 2020 9263.000000 279651.00000 5120.000000 13171.000000
18 2021 9470.599609 272715.62500 5903.904785 14345.327148
19 2022 9644.968750 277320.56250 6824.806152 14737.408203
20 2023 9760.621094 281679.71875 7970.797363 15138.805664
21 2024 9985.249023 285492.00000 9044.349609 15512.595703
22 2025 10194.104492 287971.68750 10409.479492 15828.724609
23 2026 10384.377930 288766.31250 12293.153320 16377.260742
24 2027 10567.852539 291450.31250 14324.307617 16724.646484
25 2028 10755.415039 293692.59375 16386.732422 17069.849609
26 2029 10966.658203 295504.28125 18308.341797 17407.250000
27 2030 11167.495117 296954.62500 20062.207031 17740.669922
In [15]:
from keras.models import Sequential
from keras.layers import LSTM, Dense

cols = ["Total Energy Consumption(10000 tons of SCE)",
        "Proportion of Coal(%)",
        "Proportion of Petroleum(%)", 
        "Proportion of Natural Gas(%)",
        "Proportion of Primary  Electricity and  Other Energy(%)",
        "Consumption of Coal(10000 tons)", 
        "Consumption of Coke(10000 tons)",
        "Consumption of Crude Oil(10000 tons)",
        "Consumption of Gasoline(10000 tons)",
        "Consumption of Kerosene(10000 tons)",
        "Consumption of Diesel Oil(10000 tons)",
        "Consumption of Fuel Oil(10000 tons)",
        "Consumption of Natural Gas(100 million cu.m)",
        "Consumption of Electricity(100 million kwh)"]

LSTM_Results = []

for col in cols:

    # Load data
    target_column = col
    df = pd.read_excel("D:\Jupyter Directory\Dissertation\Dissertation Datasets\Ones I will use\Annual Total Energy Consumption.xls")

    # Set the index
    df.set_index("Database：Annual", inplace=True)

    # Prepare the data
    new_df = df.T[["Indicators", target_column]].reset_index(drop=True)
    new_df = new_df.rename(columns={"Indicators": "Year"}).sort_values(by="Year")
    new_df["Year"] = new_df["Year"].astype("int")
    new_df.dropna(inplace=True)

    # Select the target column
    y = new_df[target_column].values

    # Normalize the data
    scaler = MinMaxScaler()
    y_scaled = scaler.fit_transform(y.reshape(-1, 1))

    # Prepare the data for LSTM
    look_back = 5  # Number of previous time steps to use for prediction
    X = []
    y_lstm = []
    for i in range(len(y_scaled) - look_back):
        X.append(y_scaled[i:(i + look_back)])
        y_lstm.append(y_scaled[i + look_back])
    X, y_lstm = np.array(X), np.array(y_lstm)

    # Reshape the data for LSTM (samples, time steps, features)
    X = X.reshape(X.shape[0], X.shape[1], 1)

    # Build the LSTM model
    model = Sequential()
    model.add(LSTM(units=50, return_sequences=True, input_shape=(X.shape[1], 1)))
    model.add(LSTM(units=50, return_sequences=False))
    model.add(Dense(units=1))
    model.compile(optimizer="adam", loss="mean_squared_error")

    # Train the model
    model.fit(X, y_lstm, epochs=100, batch_size=32)

    # Forecast the next 10 data points
    last_sequence = y_scaled[-look_back:]
    forecast_scaled = []
    for _ in range(10):
        input_data = np.array([last_sequence[-look_back:]])
        input_data = input_data.reshape((1, look_back, 1))
        forecast_scaled.append(model.predict(input_data)[0, 0])
        last_sequence = np.append(last_sequence, forecast_scaled[-1])

    # Inverse transform the forecasted data
    forecast = scaler.inverse_transform(np.array(forecast_scaled).reshape(-1, 1)).flatten()

    # Append the forecasted values to the original data
    extended_t = np.concatenate((new_df["Year"], np.arange(new_df["Year"].iloc[-1] + 1, new_df["Year"].iloc[-1] + 11)))
    extended_y = np.concatenate((y, forecast))

    Result = pd.DataFrame({"Year": extended_t, target_column: extended_y})

    LSTM_Results.append(Result)

    # Plot original data and forecasted values
    plt.figure(figsize=(10, 6))
    plt.plot(new_df["Year"], y, color="dodgerblue", linewidth=2, label="Original Data")
    plt.plot(extended_t[-10:], forecast, linestyle="dashed", color="darkorange", label="Forecasted Values")
    plt.xlabel("Year")
    plt.ylabel(target_column)
    plt.title("LSTM Forecast")
    plt.grid(True)
    plt.legend()
    plt.show()
    
Final_LSTM_Results2 = pd.concat(LSTM_Results, axis=1)

Final_LSTM_Results2.columns.values[0] = "year"

# Assuming 'df' is your DataFrame
cols_to_drop = [col for col in Final_LSTM_Results2.columns if col == "Year"][1:]
Final_LSTM_Results2.drop(columns=cols_to_drop, inplace=True)

Final_LSTM_Results2

Epoch 1/100
1/1 [==============================] - 2s 2s/step - loss: 0.4724
Epoch 2/100
1/1 [==============================] - 0s 4ms/step - loss: 0.4347
Epoch 3/100
1/1 [==============================] - 0s 5ms/step - loss: 0.3977
Epoch 4/100
1/1 [==============================] - 0s 4ms/step - loss: 0.3611
Epoch 5/100
1/1 [==============================] - 0s 4ms/step - loss: 0.3249
Epoch 6/100
1/1 [==============================] - 0s 5ms/step - loss: 0.2890
Epoch 7/100
1/1 [==============================] - 0s 4ms/step - loss: 0.2534
Epoch 8/100
1/1 [==============================] - 0s 4ms/step - loss: 0.2181
Epoch 9/100
1/1 [==============================] - 0s 4ms/step - loss: 0.1833
Epoch 10/100
1/1 [==============================] - 0s 3ms/step - loss: 0.1494
Epoch 11/100
1/1 [==============================] - 0s 4ms/step - loss: 0.1168
Epoch 12/100
1/1 [==============================] - 0s 5ms/step - loss: 0.0861
Epoch 13/100
1/1 [==============================] - 0s 3ms/step - loss: 0.0583
Epoch 14/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0346
Epoch 15/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0161
Epoch 16/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0045
Epoch 17/100
1/1 [==============================] - 0s 4ms/step - loss: 9.4742e-04
Epoch 18/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0056
Epoch 19/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0164
Epoch 20/100
1/1 [==============================] - 0s 5ms/step - loss: 0.0285
Epoch 21/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0370
Epoch 22/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0393
Epoch 23/100
1/1 [==============================] - 0s 3ms/step - loss: 0.0360
Epoch 24/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0289
Epoch 25/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0206
Epoch 26/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0128
Epoch 27/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0068
Epoch 28/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0030
Epoch 29/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0013
Epoch 30/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0012
Epoch 31/100
1/1 [==============================] - 0s 5ms/step - loss: 0.0022
Epoch 32/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0038
Epoch 33/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0056
Epoch 34/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0071
Epoch 35/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0082
Epoch 36/100
1/1 [==============================] - 0s 5ms/step - loss: 0.0088
Epoch 37/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0089
Epoch 38/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0084
Epoch 39/100
1/1 [==============================] - 0s 5ms/step - loss: 0.0075
Epoch 40/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0064
Epoch 41/100
1/1 [==============================] - 0s 5ms/step - loss: 0.0051
Epoch 42/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0038
Epoch 43/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0027
Epoch 44/100
1/1 [==============================] - 0s 6ms/step - loss: 0.0018
Epoch 45/100
1/1 [==============================] - 0s 5ms/step - loss: 0.0013
Epoch 46/100
1/1 [==============================] - 0s 5ms/step - loss: 0.0011
Epoch 47/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0012
Epoch 48/100
1/1 [==============================] - 0s 5ms/step - loss: 0.0015
Epoch 49/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0019
Epoch 50/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0023
Epoch 51/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0026
Epoch 52/100
1/1 [==============================] - 0s 5ms/step - loss: 0.0027
Epoch 53/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0027
Epoch 54/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0025
Epoch 55/100
1/1 [==============================] - 0s 5ms/step - loss: 0.0022
Epoch 56/100
1/1 [==============================] - 0s 5ms/step - loss: 0.0018
Epoch 57/100
1/1 [==============================] - 0s 3ms/step - loss: 0.0015
Epoch 58/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0013
Epoch 59/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0011
Epoch 60/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0011
Epoch 61/100
1/1 [==============================] - 0s 5ms/step - loss: 0.0011
Epoch 62/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0012
Epoch 63/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0013
Epoch 64/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0014
Epoch 65/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0014
Epoch 66/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0014
Epoch 67/100
1/1 [==============================] - 0s 3ms/step - loss: 0.0014
Epoch 68/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0014
Epoch 69/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0013
Epoch 70/100
1/1 [==============================] - 0s 5ms/step - loss: 0.0012
Epoch 71/100
1/1 [==============================] - 0s 5ms/step - loss: 0.0011
Epoch 72/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0011
Epoch 73/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0010
Epoch 74/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0010
Epoch 75/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0010
Epoch 76/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0010
Epoch 77/100
1/1 [==============================] - 0s 3ms/step - loss: 0.0011
Epoch 78/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0011
Epoch 79/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0011
Epoch 80/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0011
Epoch 81/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0011
Epoch 82/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0010
Epoch 83/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0010
Epoch 84/100
1/1 [==============================] - 0s 3ms/step - loss: 9.9447e-04
Epoch 85/100
1/1 [==============================] - 0s 4ms/step - loss: 9.7548e-04
Epoch 86/100
1/1 [==============================] - 0s 4ms/step - loss: 9.6420e-04
Epoch 87/100
1/1 [==============================] - 0s 3ms/step - loss: 9.6036e-04
Epoch 88/100
1/1 [==============================] - 0s 4ms/step - loss: 9.6212e-04
Epoch 89/100
1/1 [==============================] - 0s 4ms/step - loss: 9.6677e-04
Epoch 90/100
1/1 [==============================] - 0s 5ms/step - loss: 9.7150e-04
Epoch 91/100
1/1 [==============================] - 0s 4ms/step - loss: 9.7403e-04
Epoch 92/100
1/1 [==============================] - 0s 4ms/step - loss: 9.7307e-04
Epoch 93/100
1/1 [==============================] - 0s 3ms/step - loss: 9.6841e-04
Epoch 94/100
1/1 [==============================] - 0s 4ms/step - loss: 9.6079e-04
Epoch 95/100
1/1 [==============================] - 0s 4ms/step - loss: 9.5156e-04
Epoch 96/100
1/1 [==============================] - 0s 4ms/step - loss: 9.4228e-04
Epoch 97/100
1/1 [==============================] - 0s 4ms/step - loss: 9.3429e-04
Epoch 98/100
1/1 [==============================] - 0s 4ms/step - loss: 9.2846e-04
Epoch 99/100
1/1 [==============================] - 0s 4ms/step - loss: 9.2501e-04
Epoch 100/100
1/1 [==============================] - 0s 4ms/step - loss: 9.2359e-04
1/1 [==============================] - 0s 434ms/step
1/1 [==============================] - 0s 12ms/step
1/1 [==============================] - 0s 12ms/step
1/1 [==============================] - 0s 11ms/step
1/1 [==============================] - 0s 11ms/step
1/1 [==============================] - 0s 14ms/step
1/1 [==============================] - 0s 13ms/step
1/1 [==============================] - 0s 12ms/step
1/1 [==============================] - 0s 11ms/step
1/1 [==============================] - 0s 11ms/step

Epoch 1/100
1/1 [==============================] - 2s 2s/step - loss: 0.3262
Epoch 2/100
1/1 [==============================] - 0s 4ms/step - loss: 0.2955
Epoch 3/100
1/1 [==============================] - 0s 4ms/step - loss: 0.2661
Epoch 4/100
1/1 [==============================] - 0s 5ms/step - loss: 0.2380
Epoch 5/100
1/1 [==============================] - 0s 4ms/step - loss: 0.2110
Epoch 6/100
1/1 [==============================] - 0s 4ms/step - loss: 0.1851
Epoch 7/100
1/1 [==============================] - 0s 4ms/step - loss: 0.1604
Epoch 8/100
1/1 [==============================] - 0s 4ms/step - loss: 0.1371
Epoch 9/100
1/1 [==============================] - 0s 3ms/step - loss: 0.1152
Epoch 10/100
1/1 [==============================] - 0s 5ms/step - loss: 0.0953
Epoch 11/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0777
Epoch 12/100
1/1 [==============================] - 0s 3ms/step - loss: 0.0633
Epoch 13/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0526
Epoch 14/100
1/1 [==============================] - 0s 5ms/step - loss: 0.0465
Epoch 15/100
1/1 [==============================] - 0s 3ms/step - loss: 0.0454
Epoch 16/100
1/1 [==============================] - 0s 3ms/step - loss: 0.0489
Epoch 17/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0553
Epoch 18/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0616
Epoch 19/100
1/1 [==============================] - 0s 3ms/step - loss: 0.0652
Epoch 20/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0651
Epoch 21/100
1/1 [==============================] - 0s 5ms/step - loss: 0.0618
Epoch 22/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0567
Epoch 23/100
1/1 [==============================] - 0s 5ms/step - loss: 0.0512
Epoch 24/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0462
Epoch 25/100
1/1 [==============================] - 0s 5ms/step - loss: 0.0426
Epoch 26/100
1/1 [==============================] - 0s 5ms/step - loss: 0.0403
Epoch 27/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0392
Epoch 28/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0391
Epoch 29/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0395
Epoch 30/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0401
Epoch 31/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0407
Epoch 32/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0409
Epoch 33/100
1/1 [==============================] - 0s 5ms/step - loss: 0.0408
Epoch 34/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0402
Epoch 35/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0393
Epoch 36/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0379
Epoch 37/100
1/1 [==============================] - 0s 5ms/step - loss: 0.0364
Epoch 38/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0347
Epoch 39/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0331
Epoch 40/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0317
Epoch 41/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0305
Epoch 42/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0297
Epoch 43/100
1/1 [==============================] - 0s 5ms/step - loss: 0.0291
Epoch 44/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0287
Epoch 45/100
1/1 [==============================] - 0s 5ms/step - loss: 0.0284
Epoch 46/100
1/1 [==============================] - 0s 5ms/step - loss: 0.0281
Epoch 47/100
1/1 [==============================] - 0s 5ms/step - loss: 0.0276
Epoch 48/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0269
Epoch 49/100
1/1 [==============================] - 0s 6ms/step - loss: 0.0260
Epoch 50/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0250
Epoch 51/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0239
Epoch 52/100
1/1 [==============================] - 0s 3ms/step - loss: 0.0230
Epoch 53/100
1/1 [==============================] - 0s 5ms/step - loss: 0.0221
Epoch 54/100
1/1 [==============================] - 0s 5ms/step - loss: 0.0214
Epoch 55/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0208
Epoch 56/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0203
Epoch 57/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0198
Epoch 58/100
1/1 [==============================] - 0s 5ms/step - loss: 0.0193
Epoch 59/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0186
Epoch 60/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0180
Epoch 61/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0173
Epoch 62/100
1/1 [==============================] - 0s 5ms/step - loss: 0.0165
Epoch 63/100
1/1 [==============================] - 0s 5ms/step - loss: 0.0158
Epoch 64/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0152
Epoch 65/100
1/1 [==============================] - 0s 5ms/step - loss: 0.0146
Epoch 66/100
1/1 [==============================] - 0s 5ms/step - loss: 0.0141
Epoch 67/100
1/1 [==============================] - 0s 5ms/step - loss: 0.0137
Epoch 68/100
1/1 [==============================] - 0s 5ms/step - loss: 0.0132
Epoch 69/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0127
Epoch 70/100
1/1 [==============================] - 0s 5ms/step - loss: 0.0122
Epoch 71/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0117
Epoch 72/100
1/1 [==============================] - 0s 6ms/step - loss: 0.0112
Epoch 73/100
1/1 [==============================] - 0s 5ms/step - loss: 0.0107
Epoch 74/100
1/1 [==============================] - 0s 5ms/step - loss: 0.0103
Epoch 75/100
1/1 [==============================] - 0s 5ms/step - loss: 0.0100
Epoch 76/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0097
Epoch 77/100
1/1 [==============================] - 0s 5ms/step - loss: 0.0093
Epoch 78/100
1/1 [==============================] - 0s 5ms/step - loss: 0.0090
Epoch 79/100
1/1 [==============================] - 0s 6ms/step - loss: 0.0087
Epoch 80/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0084
Epoch 81/100
1/1 [==============================] - 0s 6ms/step - loss: 0.0081
Epoch 82/100
1/1 [==============================] - 0s 5ms/step - loss: 0.0078
Epoch 83/100
1/1 [==============================] - 0s 5ms/step - loss: 0.0076
Epoch 84/100
1/1 [==============================] - 0s 5ms/step - loss: 0.0074
Epoch 85/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0072
Epoch 86/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0070
Epoch 87/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0068
Epoch 88/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0067
Epoch 89/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0065
Epoch 90/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0064
Epoch 91/100
1/1 [==============================] - 0s 5ms/step - loss: 0.0063
Epoch 92/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0062
Epoch 93/100
1/1 [==============================] - 0s 5ms/step - loss: 0.0061
Epoch 94/100
1/1 [==============================] - 0s 3ms/step - loss: 0.0061
Epoch 95/100
1/1 [==============================] - 0s 5ms/step - loss: 0.0060
Epoch 96/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0059
Epoch 97/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0059
Epoch 98/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0058
Epoch 99/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0058
Epoch 100/100
1/1 [==============================] - 0s 5ms/step - loss: 0.0057
1/1 [==============================] - 0s 436ms/step
1/1 [==============================] - 0s 12ms/step
1/1 [==============================] - 0s 11ms/step
1/1 [==============================] - 0s 12ms/step
1/1 [==============================] - 0s 11ms/step
1/1 [==============================] - 0s 12ms/step
1/1 [==============================] - 0s 12ms/step
1/1 [==============================] - 0s 11ms/step
1/1 [==============================] - 0s 12ms/step
1/1 [==============================] - 0s 12ms/step

Epoch 1/100
1/1 [==============================] - 2s 2s/step - loss: 0.2097
Epoch 2/100
1/1 [==============================] - 0s 4ms/step - loss: 0.1966
Epoch 3/100
1/1 [==============================] - 0s 3ms/step - loss: 0.1840
Epoch 4/100
1/1 [==============================] - 0s 4ms/step - loss: 0.1719
Epoch 5/100
1/1 [==============================] - 0s 5ms/step - loss: 0.1600
Epoch 6/100
1/1 [==============================] - 0s 3ms/step - loss: 0.1485
Epoch 7/100
1/1 [==============================] - 0s 4ms/step - loss: 0.1372
Epoch 8/100
1/1 [==============================] - 0s 5ms/step - loss: 0.1261
Epoch 9/100
1/1 [==============================] - 0s 4ms/step - loss: 0.1154
Epoch 10/100
1/1 [==============================] - 0s 4ms/step - loss: 0.1051
Epoch 11/100
1/1 [==============================] - 0s 3ms/step - loss: 0.0953
Epoch 12/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0864
Epoch 13/100
1/1 [==============================] - 0s 3ms/step - loss: 0.0785
Epoch 14/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0719
Epoch 15/100
1/1 [==============================] - 0s 5ms/step - loss: 0.0671
Epoch 16/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0644
Epoch 17/100
1/1 [==============================] - 0s 5ms/step - loss: 0.0639
Epoch 18/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0654
Epoch 19/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0679
Epoch 20/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0703
Epoch 21/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0715
Epoch 22/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0713
Epoch 23/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0699
Epoch 24/100
1/1 [==============================] - 0s 5ms/step - loss: 0.0678
Epoch 25/100
1/1 [==============================] - 0s 5ms/step - loss: 0.0654
Epoch 26/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0632
Epoch 27/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0613
Epoch 28/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0600
Epoch 29/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0592
Epoch 30/100
1/1 [==============================] - 0s 3ms/step - loss: 0.0588
Epoch 31/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0587
Epoch 32/100
1/1 [==============================] - 0s 5ms/step - loss: 0.0587
Epoch 33/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0589
Epoch 34/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0590
Epoch 35/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0590
Epoch 36/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0589
Epoch 37/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0588
Epoch 38/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0584
Epoch 39/100
1/1 [==============================] - 0s 3ms/step - loss: 0.0580
Epoch 40/100
1/1 [==============================] - 0s 5ms/step - loss: 0.0576
Epoch 41/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0570
Epoch 42/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0565
Epoch 43/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0560
Epoch 44/100
1/1 [==============================] - 0s 5ms/step - loss: 0.0556
Epoch 45/100
1/1 [==============================] - 0s 5ms/step - loss: 0.0552
Epoch 46/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0549
Epoch 47/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0546
Epoch 48/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0544
Epoch 49/100
1/1 [==============================] - 0s 5ms/step - loss: 0.0543
Epoch 50/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0541
Epoch 51/100
1/1 [==============================] - 0s 3ms/step - loss: 0.0540
Epoch 52/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0538
Epoch 53/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0535
Epoch 54/100
1/1 [==============================] - 0s 5ms/step - loss: 0.0532
Epoch 55/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0529
Epoch 56/100
1/1 [==============================] - 0s 3ms/step - loss: 0.0526
Epoch 57/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0522
Epoch 58/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0519
Epoch 59/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0516
Epoch 60/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0513
Epoch 61/100
1/1 [==============================] - 0s 5ms/step - loss: 0.0510
Epoch 62/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0507
Epoch 63/100
1/1 [==============================] - 0s 5ms/step - loss: 0.0504
Epoch 64/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0501
Epoch 65/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0498
Epoch 66/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0495
Epoch 67/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0492
Epoch 68/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0488
Epoch 69/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0484
Epoch 70/100
1/1 [==============================] - 0s 5ms/step - loss: 0.0480
Epoch 71/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0476
Epoch 72/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0471
Epoch 73/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0467
Epoch 74/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0462
Epoch 75/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0458
Epoch 76/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0453
Epoch 77/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0448
Epoch 78/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0443
Epoch 79/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0437
Epoch 80/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0432
Epoch 81/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0426
Epoch 82/100
1/1 [==============================] - 0s 5ms/step - loss: 0.0419
Epoch 83/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0413
Epoch 84/100
1/1 [==============================] - 0s 5ms/step - loss: 0.0406
Epoch 85/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0399
Epoch 86/100
1/1 [==============================] - 0s 5ms/step - loss: 0.0392
Epoch 87/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0385
Epoch 88/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0377
Epoch 89/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0369
Epoch 90/100
1/1 [==============================] - 0s 3ms/step - loss: 0.0360
Epoch 91/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0351
Epoch 92/100
1/1 [==============================] - 0s 5ms/step - loss: 0.0342
Epoch 93/100
1/1 [==============================] - 0s 3ms/step - loss: 0.0333
Epoch 94/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0323
Epoch 95/100
1/1 [==============================] - 0s 3ms/step - loss: 0.0313
Epoch 96/100
1/1 [==============================] - 0s 3ms/step - loss: 0.0303
Epoch 97/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0292
Epoch 98/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0281
Epoch 99/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0270
Epoch 100/100
1/1 [==============================] - 0s 3ms/step - loss: 0.0260
1/1 [==============================] - 0s 439ms/step
1/1 [==============================] - 0s 11ms/step
1/1 [==============================] - 0s 12ms/step
1/1 [==============================] - 0s 13ms/step
1/1 [==============================] - 0s 11ms/step
1/1 [==============================] - 0s 12ms/step
1/1 [==============================] - 0s 11ms/step
1/1 [==============================] - 0s 11ms/step
1/1 [==============================] - 0s 11ms/step
1/1 [==============================] - 0s 11ms/step

Epoch 1/100
1/1 [==============================] - 2s 2s/step - loss: 0.3607
Epoch 2/100
1/1 [==============================] - 0s 4ms/step - loss: 0.3376
Epoch 3/100
1/1 [==============================] - 0s 4ms/step - loss: 0.3147
Epoch 4/100
1/1 [==============================] - 0s 5ms/step - loss: 0.2919
Epoch 5/100
1/1 [==============================] - 0s 5ms/step - loss: 0.2690
Epoch 6/100
1/1 [==============================] - 0s 3ms/step - loss: 0.2461
Epoch 7/100
1/1 [==============================] - 0s 3ms/step - loss: 0.2229
Epoch 8/100
1/1 [==============================] - 0s 4ms/step - loss: 0.1996
Epoch 9/100
1/1 [==============================] - 0s 4ms/step - loss: 0.1761
Epoch 10/100
1/1 [==============================] - 0s 4ms/step - loss: 0.1526
Epoch 11/100
1/1 [==============================] - 0s 3ms/step - loss: 0.1292
Epoch 12/100
1/1 [==============================] - 0s 3ms/step - loss: 0.1062
Epoch 13/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0840
Epoch 14/100
1/1 [==============================] - 0s 3ms/step - loss: 0.0630
Epoch 15/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0441
Epoch 16/100
1/1 [==============================] - 0s 3ms/step - loss: 0.0279
Epoch 17/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0156
Epoch 18/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0083
Epoch 19/100
1/1 [==============================] - 0s 3ms/step - loss: 0.0067
Epoch 20/100
1/1 [==============================] - 0s 5ms/step - loss: 0.0109
Epoch 21/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0192
Epoch 22/100
1/1 [==============================] - 0s 5ms/step - loss: 0.0278
Epoch 23/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0334
Epoch 24/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0342
Epoch 25/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0310
Epoch 26/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0253
Epoch 27/100
1/1 [==============================] - 0s 3ms/step - loss: 0.0189
Epoch 28/100
1/1 [==============================] - 0s 3ms/step - loss: 0.0132
Epoch 29/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0090
Epoch 30/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0065
Epoch 31/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0054
Epoch 32/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0056
Epoch 33/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0065
Epoch 34/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0077
Epoch 35/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0089
Epoch 36/100
1/1 [==============================] - 0s 3ms/step - loss: 0.0099
Epoch 37/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0104
Epoch 38/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0106
Epoch 39/100
1/1 [==============================] - 0s 3ms/step - loss: 0.0103
Epoch 40/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0096
Epoch 41/100
1/1 [==============================] - 0s 5ms/step - loss: 0.0086
Epoch 42/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0075
Epoch 43/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0063
Epoch 44/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0052
Epoch 45/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0043
Epoch 46/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0036
Epoch 47/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0033
Epoch 48/100
1/1 [==============================] - 0s 3ms/step - loss: 0.0032
Epoch 49/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0033
Epoch 50/100
1/1 [==============================] - 0s 5ms/step - loss: 0.0036
Epoch 51/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0038
Epoch 52/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0040
Epoch 53/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0040
Epoch 54/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0038
Epoch 55/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0035
Epoch 56/100
1/1 [==============================] - 0s 5ms/step - loss: 0.0031
Epoch 57/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0027
Epoch 58/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0023
Epoch 59/100
1/1 [==============================] - 0s 3ms/step - loss: 0.0020
Epoch 60/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0019
Epoch 61/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0018
Epoch 62/100
1/1 [==============================] - 0s 3ms/step - loss: 0.0018
Epoch 63/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0018
Epoch 64/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0019
Epoch 65/100
1/1 [==============================] - 0s 5ms/step - loss: 0.0019
Epoch 66/100
1/1 [==============================] - 0s 5ms/step - loss: 0.0018
Epoch 67/100
1/1 [==============================] - 0s 5ms/step - loss: 0.0018
Epoch 68/100
1/1 [==============================] - 0s 3ms/step - loss: 0.0017
Epoch 69/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0015
Epoch 70/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0014
Epoch 71/100
1/1 [==============================] - 0s 5ms/step - loss: 0.0013
Epoch 72/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0012
Epoch 73/100
1/1 [==============================] - 0s 5ms/step - loss: 0.0012
Epoch 74/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0012
Epoch 75/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0012
Epoch 76/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0012
Epoch 77/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0012
Epoch 78/100
1/1 [==============================] - 0s 5ms/step - loss: 0.0012
Epoch 79/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0012
Epoch 80/100
1/1 [==============================] - 0s 3ms/step - loss: 0.0011
Epoch 81/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0011
Epoch 82/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0010
Epoch 83/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0010
Epoch 84/100
1/1 [==============================] - 0s 4ms/step - loss: 9.9206e-04
Epoch 85/100
1/1 [==============================] - 0s 4ms/step - loss: 9.8296e-04
Epoch 86/100
1/1 [==============================] - 0s 4ms/step - loss: 9.8328e-04
Epoch 87/100
1/1 [==============================] - 0s 3ms/step - loss: 9.8910e-04
Epoch 88/100
1/1 [==============================] - 0s 5ms/step - loss: 9.9598e-04
Epoch 89/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0010
Epoch 90/100
1/1 [==============================] - 0s 3ms/step - loss: 9.9999e-04
Epoch 91/100
1/1 [==============================] - 0s 3ms/step - loss: 9.9483e-04
Epoch 92/100
1/1 [==============================] - 0s 4ms/step - loss: 9.8617e-04
Epoch 93/100
1/1 [==============================] - 0s 5ms/step - loss: 9.7627e-04
Epoch 94/100
1/1 [==============================] - 0s 4ms/step - loss: 9.6750e-04
Epoch 95/100
1/1 [==============================] - 0s 3ms/step - loss: 9.6163e-04
Epoch 96/100
1/1 [==============================] - 0s 4ms/step - loss: 9.5942e-04
Epoch 97/100
1/1 [==============================] - 0s 4ms/step - loss: 9.6050e-04
Epoch 98/100
1/1 [==============================] - 0s 4ms/step - loss: 9.6366e-04
Epoch 99/100
1/1 [==============================] - 0s 4ms/step - loss: 9.6733e-04
Epoch 100/100
1/1 [==============================] - 0s 5ms/step - loss: 9.7009e-04
1/1 [==============================] - 0s 435ms/step
1/1 [==============================] - 0s 11ms/step
1/1 [==============================] - 0s 11ms/step
1/1 [==============================] - 0s 12ms/step
1/1 [==============================] - 0s 12ms/step
1/1 [==============================] - 0s 10ms/step
1/1 [==============================] - 0s 12ms/step
1/1 [==============================] - 0s 12ms/step
1/1 [==============================] - 0s 11ms/step
1/1 [==============================] - 0s 12ms/step

Epoch 1/100
1/1 [==============================] - 3s 3s/step - loss: 0.3375
Epoch 2/100
1/1 [==============================] - 0s 3ms/step - loss: 0.3163
Epoch 3/100
1/1 [==============================] - 0s 5ms/step - loss: 0.2957
Epoch 4/100
1/1 [==============================] - 0s 5ms/step - loss: 0.2755
Epoch 5/100
1/1 [==============================] - 0s 3ms/step - loss: 0.2556
Epoch 6/100
1/1 [==============================] - 0s 4ms/step - loss: 0.2358
Epoch 7/100
1/1 [==============================] - 0s 4ms/step - loss: 0.2159
Epoch 8/100
1/1 [==============================] - 0s 5ms/step - loss: 0.1961
Epoch 9/100
1/1 [==============================] - 0s 5ms/step - loss: 0.1762
Epoch 10/100
1/1 [==============================] - 0s 5ms/step - loss: 0.1562
Epoch 11/100
1/1 [==============================] - 0s 5ms/step - loss: 0.1363
Epoch 12/100
1/1 [==============================] - 0s 4ms/step - loss: 0.1165
Epoch 13/100
1/1 [==============================] - 0s 5ms/step - loss: 0.0972
Epoch 14/100
1/1 [==============================] - 0s 6ms/step - loss: 0.0786
Epoch 15/100
1/1 [==============================] - 0s 5ms/step - loss: 0.0612
Epoch 16/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0456
Epoch 17/100
1/1 [==============================] - 0s 5ms/step - loss: 0.0325
Epoch 18/100
1/1 [==============================] - 0s 5ms/step - loss: 0.0227
Epoch 19/100
1/1 [==============================] - 0s 5ms/step - loss: 0.0171
Epoch 20/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0163
Epoch 21/100
1/1 [==============================] - 0s 5ms/step - loss: 0.0200
Epoch 22/100
1/1 [==============================] - 0s 5ms/step - loss: 0.0267
Epoch 23/100
1/1 [==============================] - 0s 5ms/step - loss: 0.0334
Epoch 24/100
1/1 [==============================] - 0s 5ms/step - loss: 0.0374
Epoch 25/100
1/1 [==============================] - 0s 5ms/step - loss: 0.0376
Epoch 26/100
1/1 [==============================] - 0s 5ms/step - loss: 0.0344
Epoch 27/100
1/1 [==============================] - 0s 5ms/step - loss: 0.0292
Epoch 28/100
1/1 [==============================] - 0s 5ms/step - loss: 0.0235
Epoch 29/100
1/1 [==============================] - 0s 5ms/step - loss: 0.0185
Epoch 30/100
1/1 [==============================] - 0s 5ms/step - loss: 0.0146
Epoch 31/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0123
Epoch 32/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0112
Epoch 33/100
1/1 [==============================] - 0s 5ms/step - loss: 0.0112
Epoch 34/100
1/1 [==============================] - 0s 5ms/step - loss: 0.0117
Epoch 35/100
1/1 [==============================] - 0s 6ms/step - loss: 0.0126
Epoch 36/100
1/1 [==============================] - 0s 6ms/step - loss: 0.0134
Epoch 37/100
1/1 [==============================] - 0s 5ms/step - loss: 0.0140
Epoch 38/100
1/1 [==============================] - 0s 5ms/step - loss: 0.0142
Epoch 39/100
1/1 [==============================] - 0s 5ms/step - loss: 0.0140
Epoch 40/100
1/1 [==============================] - 0s 5ms/step - loss: 0.0134
Epoch 41/100
1/1 [==============================] - 0s 5ms/step - loss: 0.0125
Epoch 42/100
1/1 [==============================] - 0s 7ms/step - loss: 0.0114
Epoch 43/100
1/1 [==============================] - 0s 5ms/step - loss: 0.0101
Epoch 44/100
1/1 [==============================] - 0s 5ms/step - loss: 0.0089
Epoch 45/100
1/1 [==============================] - 0s 6ms/step - loss: 0.0078
Epoch 46/100
1/1 [==============================] - 0s 6ms/step - loss: 0.0070
Epoch 47/100
1/1 [==============================] - 0s 6ms/step - loss: 0.0064
Epoch 48/100
1/1 [==============================] - 0s 6ms/step - loss: 0.0062
Epoch 49/100
1/1 [==============================] - 0s 5ms/step - loss: 0.0062
Epoch 50/100
1/1 [==============================] - 0s 5ms/step - loss: 0.0064
Epoch 51/100
1/1 [==============================] - 0s 5ms/step - loss: 0.0067
Epoch 52/100
1/1 [==============================] - 0s 6ms/step - loss: 0.0068
Epoch 53/100
1/1 [==============================] - 0s 5ms/step - loss: 0.0068
Epoch 54/100
1/1 [==============================] - 0s 5ms/step - loss: 0.0067
Epoch 55/100
1/1 [==============================] - 0s 6ms/step - loss: 0.0064
Epoch 56/100
1/1 [==============================] - 0s 5ms/step - loss: 0.0060
Epoch 57/100
1/1 [==============================] - 0s 5ms/step - loss: 0.0056
Epoch 58/100
1/1 [==============================] - 0s 5ms/step - loss: 0.0053
Epoch 59/100
1/1 [==============================] - 0s 5ms/step - loss: 0.0050
Epoch 60/100
1/1 [==============================] - 0s 5ms/step - loss: 0.0049
Epoch 61/100
1/1 [==============================] - 0s 5ms/step - loss: 0.0050
Epoch 62/100
1/1 [==============================] - 0s 5ms/step - loss: 0.0050
Epoch 63/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0051
Epoch 64/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0052
Epoch 65/100
1/1 [==============================] - 0s 5ms/step - loss: 0.0052
Epoch 66/100
1/1 [==============================] - 0s 5ms/step - loss: 0.0052
Epoch 67/100
1/1 [==============================] - 0s 5ms/step - loss: 0.0052
Epoch 68/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0051
Epoch 69/100
1/1 [==============================] - 0s 5ms/step - loss: 0.0050
Epoch 70/100
1/1 [==============================] - 0s 5ms/step - loss: 0.0049
Epoch 71/100
1/1 [==============================] - 0s 5ms/step - loss: 0.0048
Epoch 72/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0048
Epoch 73/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0048
Epoch 74/100
1/1 [==============================] - 0s 5ms/step - loss: 0.0048
Epoch 75/100
1/1 [==============================] - 0s 5ms/step - loss: 0.0048
Epoch 76/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0048
Epoch 77/100
1/1 [==============================] - 0s 5ms/step - loss: 0.0048
Epoch 78/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0048
Epoch 79/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0048
Epoch 80/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0047
Epoch 81/100
1/1 [==============================] - 0s 5ms/step - loss: 0.0047
Epoch 82/100
1/1 [==============================] - 0s 5ms/step - loss: 0.0047
Epoch 83/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0046
Epoch 84/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0046
Epoch 85/100
1/1 [==============================] - 0s 5ms/step - loss: 0.0046
Epoch 86/100
1/1 [==============================] - 0s 5ms/step - loss: 0.0046
Epoch 87/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0046
Epoch 88/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0046
Epoch 89/100
1/1 [==============================] - 0s 5ms/step - loss: 0.0046
Epoch 90/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0046
Epoch 91/100
1/1 [==============================] - 0s 5ms/step - loss: 0.0045
Epoch 92/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0045
Epoch 93/100
1/1 [==============================] - 0s 5ms/step - loss: 0.0045
Epoch 94/100
1/1 [==============================] - 0s 5ms/step - loss: 0.0045
Epoch 95/100
1/1 [==============================] - 0s 5ms/step - loss: 0.0045
Epoch 96/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0044
Epoch 97/100
1/1 [==============================] - 0s 5ms/step - loss: 0.0044
Epoch 98/100
1/1 [==============================] - 0s 3ms/step - loss: 0.0044
Epoch 99/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0044
Epoch 100/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0044
1/1 [==============================] - 0s 433ms/step
1/1 [==============================] - 0s 12ms/step
1/1 [==============================] - 0s 12ms/step
1/1 [==============================] - 0s 13ms/step
1/1 [==============================] - 0s 12ms/step
1/1 [==============================] - 0s 11ms/step
1/1 [==============================] - 0s 12ms/step
1/1 [==============================] - 0s 11ms/step
1/1 [==============================] - 0s 10ms/step
1/1 [==============================] - 0s 12ms/step

Epoch 1/100
1/1 [==============================] - 2s 2s/step - loss: 0.7124
Epoch 2/100
1/1 [==============================] - 0s 4ms/step - loss: 0.6657
Epoch 3/100
1/1 [==============================] - 0s 3ms/step - loss: 0.6206
Epoch 4/100
1/1 [==============================] - 0s 3ms/step - loss: 0.5766
Epoch 5/100
1/1 [==============================] - 0s 3ms/step - loss: 0.5334
Epoch 6/100
1/1 [==============================] - 0s 5ms/step - loss: 0.4908
Epoch 7/100
1/1 [==============================] - 0s 4ms/step - loss: 0.4486
Epoch 8/100
1/1 [==============================] - 0s 5ms/step - loss: 0.4065
Epoch 9/100
1/1 [==============================] - 0s 4ms/step - loss: 0.3644
Epoch 10/100
1/1 [==============================] - 0s 5ms/step - loss: 0.3223
Epoch 11/100
1/1 [==============================] - 0s 4ms/step - loss: 0.2803
Epoch 12/100
1/1 [==============================] - 0s 3ms/step - loss: 0.2387
Epoch 13/100
1/1 [==============================] - 0s 3ms/step - loss: 0.1978
Epoch 14/100
1/1 [==============================] - 0s 4ms/step - loss: 0.1583
Epoch 15/100
1/1 [==============================] - 0s 4ms/step - loss: 0.1212
Epoch 16/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0876
Epoch 17/100
1/1 [==============================] - 0s 3ms/step - loss: 0.0591
Epoch 18/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0377
Epoch 19/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0252
Epoch 20/100
1/1 [==============================] - 0s 5ms/step - loss: 0.0231
Epoch 21/100
1/1 [==============================] - 0s 5ms/step - loss: 0.0311
Epoch 22/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0454
Epoch 23/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0596
Epoch 24/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0681
Epoch 25/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0688
Epoch 26/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0630
Epoch 27/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0533
Epoch 28/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0425
Epoch 29/100
1/1 [==============================] - 0s 5ms/step - loss: 0.0328
Epoch 30/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0254
Epoch 31/100
1/1 [==============================] - 0s 3ms/step - loss: 0.0207
Epoch 32/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0185
Epoch 33/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0183
Epoch 34/100
1/1 [==============================] - 0s 3ms/step - loss: 0.0193
Epoch 35/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0210
Epoch 36/100
1/1 [==============================] - 0s 5ms/step - loss: 0.0228
Epoch 37/100
1/1 [==============================] - 0s 3ms/step - loss: 0.0243
Epoch 38/100
1/1 [==============================] - 0s 3ms/step - loss: 0.0254
Epoch 39/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0259
Epoch 40/100
1/1 [==============================] - 0s 3ms/step - loss: 0.0257
Epoch 41/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0249
Epoch 42/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0237
Epoch 43/100
1/1 [==============================] - 0s 3ms/step - loss: 0.0222
Epoch 44/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0205
Epoch 45/100
1/1 [==============================] - 0s 6ms/step - loss: 0.0189
Epoch 46/100
1/1 [==============================] - 0s 5ms/step - loss: 0.0174
Epoch 47/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0162
Epoch 48/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0153
Epoch 49/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0148
Epoch 50/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0146
Epoch 51/100
1/1 [==============================] - 0s 5ms/step - loss: 0.0148
Epoch 52/100
1/1 [==============================] - 0s 5ms/step - loss: 0.0150
Epoch 53/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0153
Epoch 54/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0156
Epoch 55/100
1/1 [==============================] - 0s 5ms/step - loss: 0.0157
Epoch 56/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0156
Epoch 57/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0154
Epoch 58/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0150
Epoch 59/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0145
Epoch 60/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0141
Epoch 61/100
1/1 [==============================] - 0s 5ms/step - loss: 0.0136
Epoch 62/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0133
Epoch 63/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0130
Epoch 64/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0129
Epoch 65/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0128
Epoch 66/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0128
Epoch 67/100
1/1 [==============================] - 0s 5ms/step - loss: 0.0128
Epoch 68/100
1/1 [==============================] - 0s 5ms/step - loss: 0.0128
Epoch 69/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0128
Epoch 70/100
1/1 [==============================] - 0s 5ms/step - loss: 0.0127
Epoch 71/100
1/1 [==============================] - 0s 5ms/step - loss: 0.0126
Epoch 72/100
1/1 [==============================] - 0s 3ms/step - loss: 0.0125
Epoch 73/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0124
Epoch 74/100
1/1 [==============================] - 0s 5ms/step - loss: 0.0122
Epoch 75/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0120
Epoch 76/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0119
Epoch 77/100
1/1 [==============================] - 0s 5ms/step - loss: 0.0118
Epoch 78/100
1/1 [==============================] - 0s 5ms/step - loss: 0.0117
Epoch 79/100
1/1 [==============================] - 0s 3ms/step - loss: 0.0116
Epoch 80/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0115
Epoch 81/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0115
Epoch 82/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0114
Epoch 83/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0114
Epoch 84/100
1/1 [==============================] - 0s 3ms/step - loss: 0.0113
Epoch 85/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0113
Epoch 86/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0112
Epoch 87/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0111
Epoch 88/100
1/1 [==============================] - 0s 3ms/step - loss: 0.0111
Epoch 89/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0110
Epoch 90/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0109
Epoch 91/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0109
Epoch 92/100
1/1 [==============================] - 0s 5ms/step - loss: 0.0108
Epoch 93/100
1/1 [==============================] - 0s 3ms/step - loss: 0.0107
Epoch 94/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0107
Epoch 95/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0106
Epoch 96/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0106
Epoch 97/100
1/1 [==============================] - 0s 3ms/step - loss: 0.0106
Epoch 98/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0105
Epoch 99/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0105
Epoch 100/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0104
1/1 [==============================] - 0s 433ms/step
1/1 [==============================] - 0s 12ms/step
1/1 [==============================] - 0s 11ms/step
1/1 [==============================] - 0s 12ms/step
1/1 [==============================] - 0s 11ms/step
1/1 [==============================] - 0s 12ms/step
1/1 [==============================] - 0s 13ms/step
1/1 [==============================] - 0s 11ms/step
1/1 [==============================] - 0s 12ms/step
1/1 [==============================] - 0s 11ms/step

Epoch 1/100
1/1 [==============================] - 2s 2s/step - loss: 0.7299
Epoch 2/100
1/1 [==============================] - 0s 3ms/step - loss: 0.6828
Epoch 3/100
1/1 [==============================] - 0s 4ms/step - loss: 0.6374
Epoch 4/100
1/1 [==============================] - 0s 4ms/step - loss: 0.5933
Epoch 5/100
1/1 [==============================] - 0s 4ms/step - loss: 0.5501
Epoch 6/100
1/1 [==============================] - 0s 4ms/step - loss: 0.5075
Epoch 7/100
1/1 [==============================] - 0s 4ms/step - loss: 0.4653
Epoch 8/100
1/1 [==============================] - 0s 5ms/step - loss: 0.4231
Epoch 9/100
1/1 [==============================] - 0s 5ms/step - loss: 0.3808
Epoch 10/100
1/1 [==============================] - 0s 5ms/step - loss: 0.3385
Epoch 11/100
1/1 [==============================] - 0s 4ms/step - loss: 0.2963
Epoch 12/100
1/1 [==============================] - 0s 4ms/step - loss: 0.2543
Epoch 13/100
1/1 [==============================] - 0s 4ms/step - loss: 0.2128
Epoch 14/100
1/1 [==============================] - 0s 5ms/step - loss: 0.1725
Epoch 15/100
1/1 [==============================] - 0s 5ms/step - loss: 0.1340
Epoch 16/100
1/1 [==============================] - 0s 5ms/step - loss: 0.0985
Epoch 17/100
1/1 [==============================] - 0s 5ms/step - loss: 0.0672
Epoch 18/100
1/1 [==============================] - 0s 3ms/step - loss: 0.0417
Epoch 19/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0241
Epoch 20/100
1/1 [==============================] - 0s 5ms/step - loss: 0.0159
Epoch 21/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0180
Epoch 22/100
1/1 [==============================] - 0s 5ms/step - loss: 0.0289
Epoch 23/100
1/1 [==============================] - 0s 5ms/step - loss: 0.0438
Epoch 24/100
1/1 [==============================] - 0s 5ms/step - loss: 0.0563
Epoch 25/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0621
Epoch 26/100
1/1 [==============================] - 0s 5ms/step - loss: 0.0605
Epoch 27/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0532
Epoch 28/100
1/1 [==============================] - 0s 5ms/step - loss: 0.0431
Epoch 29/100
1/1 [==============================] - 0s 5ms/step - loss: 0.0327
Epoch 30/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0239
Epoch 31/100
1/1 [==============================] - 0s 5ms/step - loss: 0.0174
Epoch 32/100
1/1 [==============================] - 0s 5ms/step - loss: 0.0136
Epoch 33/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0121
Epoch 34/100
1/1 [==============================] - 0s 5ms/step - loss: 0.0123
Epoch 35/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0135
Epoch 36/100
1/1 [==============================] - 0s 5ms/step - loss: 0.0153
Epoch 37/100
1/1 [==============================] - 0s 5ms/step - loss: 0.0171
Epoch 38/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0186
Epoch 39/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0195
Epoch 40/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0199
Epoch 41/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0197
Epoch 42/100
1/1 [==============================] - 0s 5ms/step - loss: 0.0189
Epoch 43/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0177
Epoch 44/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0162
Epoch 45/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0146
Epoch 46/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0130
Epoch 47/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0116
Epoch 48/100
1/1 [==============================] - 0s 5ms/step - loss: 0.0105
Epoch 49/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0098
Epoch 50/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0094
Epoch 51/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0093
Epoch 52/100
1/1 [==============================] - 0s 5ms/step - loss: 0.0095
Epoch 53/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0098
Epoch 54/100
1/1 [==============================] - 0s 5ms/step - loss: 0.0101
Epoch 55/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0104
Epoch 56/100
1/1 [==============================] - 0s 6ms/step - loss: 0.0105
Epoch 57/100
1/1 [==============================] - 0s 5ms/step - loss: 0.0104
Epoch 58/100
1/1 [==============================] - 0s 5ms/step - loss: 0.0102
Epoch 59/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0098
Epoch 60/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0094
Epoch 61/100
1/1 [==============================] - 0s 5ms/step - loss: 0.0090
Epoch 62/100
1/1 [==============================] - 0s 5ms/step - loss: 0.0086
Epoch 63/100
1/1 [==============================] - 0s 6ms/step - loss: 0.0083
Epoch 64/100
1/1 [==============================] - 0s 5ms/step - loss: 0.0081
Epoch 65/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0079
Epoch 66/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0079
Epoch 67/100
1/1 [==============================] - 0s 3ms/step - loss: 0.0079
Epoch 68/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0079
Epoch 69/100
1/1 [==============================] - 0s 5ms/step - loss: 0.0079
Epoch 70/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0079
Epoch 71/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0079
Epoch 72/100
1/1 [==============================] - 0s 3ms/step - loss: 0.0078
Epoch 73/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0077
Epoch 74/100
1/1 [==============================] - 0s 3ms/step - loss: 0.0076
Epoch 75/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0075
Epoch 76/100
1/1 [==============================] - 0s 5ms/step - loss: 0.0073
Epoch 77/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0072
Epoch 78/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0071
Epoch 79/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0070
Epoch 80/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0069
Epoch 81/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0069
Epoch 82/100
1/1 [==============================] - 0s 5ms/step - loss: 0.0069
Epoch 83/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0068
Epoch 84/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0068
Epoch 85/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0068
Epoch 86/100
1/1 [==============================] - 0s 3ms/step - loss: 0.0067
Epoch 87/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0067
Epoch 88/100
1/1 [==============================] - 0s 5ms/step - loss: 0.0066
Epoch 89/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0065
Epoch 90/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0065
Epoch 91/100
1/1 [==============================] - 0s 3ms/step - loss: 0.0064
Epoch 92/100
1/1 [==============================] - 0s 3ms/step - loss: 0.0064
Epoch 93/100
1/1 [==============================] - 0s 3ms/step - loss: 0.0063
Epoch 94/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0063
Epoch 95/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0062
Epoch 96/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0062
Epoch 97/100
1/1 [==============================] - 0s 3ms/step - loss: 0.0061
Epoch 98/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0061
Epoch 99/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0061
Epoch 100/100
1/1 [==============================] - 0s 3ms/step - loss: 0.0060
1/1 [==============================] - 0s 433ms/step
1/1 [==============================] - 0s 11ms/step
1/1 [==============================] - 0s 12ms/step
1/1 [==============================] - 0s 12ms/step
1/1 [==============================] - 0s 12ms/step
1/1 [==============================] - 0s 12ms/step
1/1 [==============================] - 0s 11ms/step
1/1 [==============================] - 0s 12ms/step
1/1 [==============================] - 0s 11ms/step
1/1 [==============================] - 0s 11ms/step

Epoch 1/100
1/1 [==============================] - 2s 2s/step - loss: 0.4005
Epoch 2/100
1/1 [==============================] - 0s 5ms/step - loss: 0.3742
Epoch 3/100
1/1 [==============================] - 0s 4ms/step - loss: 0.3481
Epoch 4/100
1/1 [==============================] - 0s 3ms/step - loss: 0.3220
Epoch 5/100
1/1 [==============================] - 0s 4ms/step - loss: 0.2959
Epoch 6/100
1/1 [==============================] - 0s 4ms/step - loss: 0.2697
Epoch 7/100
1/1 [==============================] - 0s 3ms/step - loss: 0.2433
Epoch 8/100
1/1 [==============================] - 0s 3ms/step - loss: 0.2167
Epoch 9/100
1/1 [==============================] - 0s 3ms/step - loss: 0.1899
Epoch 10/100
1/1 [==============================] - 0s 4ms/step - loss: 0.1630
Epoch 11/100
1/1 [==============================] - 0s 4ms/step - loss: 0.1362
Epoch 12/100
1/1 [==============================] - 0s 3ms/step - loss: 0.1099
Epoch 13/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0845
Epoch 14/100
1/1 [==============================] - 0s 3ms/step - loss: 0.0608
Epoch 15/100
1/1 [==============================] - 0s 3ms/step - loss: 0.0395
Epoch 16/100
1/1 [==============================] - 0s 3ms/step - loss: 0.0219
Epoch 17/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0093
Epoch 18/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0030
Epoch 19/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0039
Epoch 20/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0112
Epoch 21/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0220
Epoch 22/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0313
Epoch 23/100
1/1 [==============================] - 0s 3ms/step - loss: 0.0358
Epoch 24/100
1/1 [==============================] - 0s 3ms/step - loss: 0.0346
Epoch 25/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0293
Epoch 26/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0220
Epoch 27/100
1/1 [==============================] - 0s 5ms/step - loss: 0.0148
Epoch 28/100
1/1 [==============================] - 0s 5ms/step - loss: 0.0088
Epoch 29/100
1/1 [==============================] - 0s 5ms/step - loss: 0.0049
Epoch 30/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0029
Epoch 31/100
1/1 [==============================] - 0s 5ms/step - loss: 0.0026
Epoch 32/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0034
Epoch 33/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0048
Epoch 34/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0064
Epoch 35/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0079
Epoch 36/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0089
Epoch 37/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0095
Epoch 38/100
1/1 [==============================] - 0s 3ms/step - loss: 0.0095
Epoch 39/100
1/1 [==============================] - 0s 5ms/step - loss: 0.0090
Epoch 40/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0082
Epoch 41/100
1/1 [==============================] - 0s 5ms/step - loss: 0.0070
Epoch 42/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0058
Epoch 43/100
1/1 [==============================] - 0s 3ms/step - loss: 0.0045
Epoch 44/100
1/1 [==============================] - 0s 5ms/step - loss: 0.0034
Epoch 45/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0026
Epoch 46/100
1/1 [==============================] - 0s 5ms/step - loss: 0.0021
Epoch 47/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0020
Epoch 48/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0021
Epoch 49/100
1/1 [==============================] - 0s 5ms/step - loss: 0.0024
Epoch 50/100
1/1 [==============================] - 0s 5ms/step - loss: 0.0028
Epoch 51/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0031
Epoch 52/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0033
Epoch 53/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0033
Epoch 54/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0031
Epoch 55/100
1/1 [==============================] - 0s 5ms/step - loss: 0.0028
Epoch 56/100
1/1 [==============================] - 0s 5ms/step - loss: 0.0024
Epoch 57/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0020
Epoch 58/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0017
Epoch 59/100
1/1 [==============================] - 0s 5ms/step - loss: 0.0014
Epoch 60/100
1/1 [==============================] - 0s 3ms/step - loss: 0.0013
Epoch 61/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0013
Epoch 62/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0014
Epoch 63/100
1/1 [==============================] - 0s 5ms/step - loss: 0.0015
Epoch 64/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0015
Epoch 65/100
1/1 [==============================] - 0s 5ms/step - loss: 0.0015
Epoch 66/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0015
Epoch 67/100
1/1 [==============================] - 0s 3ms/step - loss: 0.0015
Epoch 68/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0013
Epoch 69/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0012
Epoch 70/100
1/1 [==============================] - 0s 3ms/step - loss: 0.0011
Epoch 71/100
1/1 [==============================] - 0s 3ms/step - loss: 9.8189e-04
Epoch 72/100
1/1 [==============================] - 0s 4ms/step - loss: 8.9855e-04
Epoch 73/100
1/1 [==============================] - 0s 5ms/step - loss: 8.4789e-04
Epoch 74/100
1/1 [==============================] - 0s 4ms/step - loss: 8.2701e-04
Epoch 75/100
1/1 [==============================] - 0s 4ms/step - loss: 8.2697e-04
Epoch 76/100
1/1 [==============================] - 0s 4ms/step - loss: 8.3564e-04
Epoch 77/100
1/1 [==============================] - 0s 4ms/step - loss: 8.4104e-04
Epoch 78/100
1/1 [==============================] - 0s 4ms/step - loss: 8.3434e-04
Epoch 79/100
1/1 [==============================] - 0s 4ms/step - loss: 8.1166e-04
Epoch 80/100
1/1 [==============================] - 0s 5ms/step - loss: 7.7431e-04
Epoch 81/100
1/1 [==============================] - 0s 4ms/step - loss: 7.2764e-04
Epoch 82/100
1/1 [==============================] - 0s 4ms/step - loss: 6.7893e-04
Epoch 83/100
1/1 [==============================] - 0s 4ms/step - loss: 6.3510e-04
Epoch 84/100
1/1 [==============================] - 0s 4ms/step - loss: 6.0101e-04
Epoch 85/100
1/1 [==============================] - 0s 5ms/step - loss: 5.7845e-04
Epoch 86/100
1/1 [==============================] - 0s 4ms/step - loss: 5.6628e-04
Epoch 87/100
1/1 [==============================] - 0s 4ms/step - loss: 5.6120e-04
Epoch 88/100
1/1 [==============================] - 0s 4ms/step - loss: 5.5901e-04
Epoch 89/100
1/1 [==============================] - 0s 3ms/step - loss: 5.5577e-04
Epoch 90/100
1/1 [==============================] - 0s 4ms/step - loss: 5.4884e-04
Epoch 91/100
1/1 [==============================] - 0s 3ms/step - loss: 5.3725e-04
Epoch 92/100
1/1 [==============================] - 0s 4ms/step - loss: 5.2169e-04
Epoch 93/100
1/1 [==============================] - 0s 5ms/step - loss: 5.0404e-04
Epoch 94/100
1/1 [==============================] - 0s 3ms/step - loss: 4.8666e-04
Epoch 95/100
1/1 [==============================] - 0s 3ms/step - loss: 4.7168e-04
Epoch 96/100
1/1 [==============================] - 0s 4ms/step - loss: 4.6044e-04
Epoch 97/100
1/1 [==============================] - 0s 3ms/step - loss: 4.5323e-04
Epoch 98/100
1/1 [==============================] - 0s 4ms/step - loss: 4.4933e-04
Epoch 99/100
1/1 [==============================] - 0s 5ms/step - loss: 4.4741e-04
Epoch 100/100
1/1 [==============================] - 0s 4ms/step - loss: 4.4593e-04
1/1 [==============================] - 0s 434ms/step
1/1 [==============================] - 0s 11ms/step
1/1 [==============================] - 0s 11ms/step
1/1 [==============================] - 0s 11ms/step
1/1 [==============================] - 0s 11ms/step
1/1 [==============================] - 0s 11ms/step
1/1 [==============================] - 0s 12ms/step
1/1 [==============================] - 0s 11ms/step
1/1 [==============================] - 0s 12ms/step
1/1 [==============================] - 0s 12ms/step

Epoch 1/100
1/1 [==============================] - 3s 3s/step - loss: 0.4633
Epoch 2/100
1/1 [==============================] - 0s 4ms/step - loss: 0.4351
Epoch 3/100
1/1 [==============================] - 0s 4ms/step - loss: 0.4082
Epoch 4/100
1/1 [==============================] - 0s 3ms/step - loss: 0.3822
Epoch 5/100
1/1 [==============================] - 0s 4ms/step - loss: 0.3569
Epoch 6/100
1/1 [==============================] - 0s 4ms/step - loss: 0.3318
Epoch 7/100
1/1 [==============================] - 0s 3ms/step - loss: 0.3069
Epoch 8/100
1/1 [==============================] - 0s 4ms/step - loss: 0.2820
Epoch 9/100
1/1 [==============================] - 0s 4ms/step - loss: 0.2570
Epoch 10/100
1/1 [==============================] - 0s 4ms/step - loss: 0.2318
Epoch 11/100
1/1 [==============================] - 0s 4ms/step - loss: 0.2065
Epoch 12/100
1/1 [==============================] - 0s 4ms/step - loss: 0.1811
Epoch 13/100
1/1 [==============================] - 0s 5ms/step - loss: 0.1558
Epoch 14/100
1/1 [==============================] - 0s 4ms/step - loss: 0.1308
Epoch 15/100
1/1 [==============================] - 0s 4ms/step - loss: 0.1063
Epoch 16/100
1/1 [==============================] - 0s 3ms/step - loss: 0.0830
Epoch 17/100
1/1 [==============================] - 0s 3ms/step - loss: 0.0614
Epoch 18/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0422
Epoch 19/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0265
Epoch 20/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0153
Epoch 21/100
1/1 [==============================] - 0s 3ms/step - loss: 0.0098
Epoch 22/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0105
Epoch 23/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0168
Epoch 24/100
1/1 [==============================] - 0s 3ms/step - loss: 0.0263
Epoch 25/100
1/1 [==============================] - 0s 5ms/step - loss: 0.0350
Epoch 26/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0397
Epoch 27/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0396
Epoch 28/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0354
Epoch 29/100
1/1 [==============================] - 0s 3ms/step - loss: 0.0292
Epoch 30/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0224
Epoch 31/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0166
Epoch 32/100
1/1 [==============================] - 0s 3ms/step - loss: 0.0124
Epoch 33/100
1/1 [==============================] - 0s 3ms/step - loss: 0.0099
Epoch 34/100
1/1 [==============================] - 0s 3ms/step - loss: 0.0089
Epoch 35/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0092
Epoch 36/100
1/1 [==============================] - 0s 5ms/step - loss: 0.0101
Epoch 37/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0115
Epoch 38/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0128
Epoch 39/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0139
Epoch 40/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0146
Epoch 41/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0149
Epoch 42/100
1/1 [==============================] - 0s 5ms/step - loss: 0.0148
Epoch 43/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0142
Epoch 44/100
1/1 [==============================] - 0s 5ms/step - loss: 0.0134
Epoch 45/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0124
Epoch 46/100
1/1 [==============================] - 0s 5ms/step - loss: 0.0113
Epoch 47/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0102
Epoch 48/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0093
Epoch 49/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0086
Epoch 50/100
1/1 [==============================] - 0s 3ms/step - loss: 0.0082
Epoch 51/100
1/1 [==============================] - 0s 3ms/step - loss: 0.0081
Epoch 52/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0082
Epoch 53/100
1/1 [==============================] - 0s 3ms/step - loss: 0.0084
Epoch 54/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0087
Epoch 55/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0090
Epoch 56/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0091
Epoch 57/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0091
Epoch 58/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0090
Epoch 59/100
1/1 [==============================] - 0s 5ms/step - loss: 0.0087
Epoch 60/100
1/1 [==============================] - 0s 3ms/step - loss: 0.0084
Epoch 61/100
1/1 [==============================] - 0s 3ms/step - loss: 0.0081
Epoch 62/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0078
Epoch 63/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0076
Epoch 64/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0075
Epoch 65/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0074
Epoch 66/100
1/1 [==============================] - 0s 5ms/step - loss: 0.0075
Epoch 67/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0075
Epoch 68/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0075
Epoch 69/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0076
Epoch 70/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0076
Epoch 71/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0076
Epoch 72/100
1/1 [==============================] - 0s 5ms/step - loss: 0.0075
Epoch 73/100
1/1 [==============================] - 0s 5ms/step - loss: 0.0074
Epoch 74/100
1/1 [==============================] - 0s 3ms/step - loss: 0.0073
Epoch 75/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0072
Epoch 76/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0072
Epoch 77/100
1/1 [==============================] - 0s 5ms/step - loss: 0.0071
Epoch 78/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0071
Epoch 79/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0071
Epoch 80/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0071
Epoch 81/100
1/1 [==============================] - 0s 5ms/step - loss: 0.0071
Epoch 82/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0071
Epoch 83/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0071
Epoch 84/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0071
Epoch 85/100
1/1 [==============================] - 0s 5ms/step - loss: 0.0070
Epoch 86/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0070
Epoch 87/100
1/1 [==============================] - 0s 5ms/step - loss: 0.0070
Epoch 88/100
1/1 [==============================] - 0s 3ms/step - loss: 0.0069
Epoch 89/100
1/1 [==============================] - 0s 3ms/step - loss: 0.0069
Epoch 90/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0069
Epoch 91/100
1/1 [==============================] - 0s 3ms/step - loss: 0.0069
Epoch 92/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0069
Epoch 93/100
1/1 [==============================] - 0s 5ms/step - loss: 0.0069
Epoch 94/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0068
Epoch 95/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0068
Epoch 96/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0068
Epoch 97/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0068
Epoch 98/100
1/1 [==============================] - 0s 5ms/step - loss: 0.0068
Epoch 99/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0068
Epoch 100/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0068
1/1 [==============================] - 0s 433ms/step
1/1 [==============================] - 0s 12ms/step
1/1 [==============================] - 0s 11ms/step
1/1 [==============================] - 0s 12ms/step
1/1 [==============================] - 0s 12ms/step
1/1 [==============================] - 0s 12ms/step
1/1 [==============================] - 0s 11ms/step
1/1 [==============================] - 0s 11ms/step
1/1 [==============================] - 0s 12ms/step
1/1 [==============================] - 0s 12ms/step

Epoch 1/100
1/1 [==============================] - 2s 2s/step - loss: 0.3868
Epoch 2/100
1/1 [==============================] - 0s 4ms/step - loss: 0.3607
Epoch 3/100
1/1 [==============================] - 0s 4ms/step - loss: 0.3359
Epoch 4/100
1/1 [==============================] - 0s 3ms/step - loss: 0.3120
Epoch 5/100
1/1 [==============================] - 0s 4ms/step - loss: 0.2890
Epoch 6/100
1/1 [==============================] - 0s 3ms/step - loss: 0.2665
Epoch 7/100
1/1 [==============================] - 0s 4ms/step - loss: 0.2444
Epoch 8/100
1/1 [==============================] - 0s 3ms/step - loss: 0.2226
Epoch 9/100
1/1 [==============================] - 0s 4ms/step - loss: 0.2010
Epoch 10/100
1/1 [==============================] - 0s 4ms/step - loss: 0.1795
Epoch 11/100
1/1 [==============================] - 0s 4ms/step - loss: 0.1582
Epoch 12/100
1/1 [==============================] - 0s 3ms/step - loss: 0.1372
Epoch 13/100
1/1 [==============================] - 0s 4ms/step - loss: 0.1165
Epoch 14/100
1/1 [==============================] - 0s 3ms/step - loss: 0.0964
Epoch 15/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0773
Epoch 16/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0596
Epoch 17/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0439
Epoch 18/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0308
Epoch 19/100
1/1 [==============================] - 0s 3ms/step - loss: 0.0212
Epoch 20/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0159
Epoch 21/100
1/1 [==============================] - 0s 3ms/step - loss: 0.0152
Epoch 22/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0190
Epoch 23/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0258
Epoch 24/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0328
Epoch 25/100
1/1 [==============================] - 0s 3ms/step - loss: 0.0374
Epoch 26/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0384
Epoch 27/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0359
Epoch 28/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0313
Epoch 29/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0259
Epoch 30/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0209
Epoch 31/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0169
Epoch 32/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0143
Epoch 33/100
1/1 [==============================] - 0s 3ms/step - loss: 0.0130
Epoch 34/100
1/1 [==============================] - 0s 3ms/step - loss: 0.0127
Epoch 35/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0132
Epoch 36/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0141
Epoch 37/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0151
Epoch 38/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0160
Epoch 39/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0165
Epoch 40/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0168
Epoch 41/100
1/1 [==============================] - 0s 3ms/step - loss: 0.0166
Epoch 42/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0162
Epoch 43/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0154
Epoch 44/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0145
Epoch 45/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0134
Epoch 46/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0124
Epoch 47/100
1/1 [==============================] - 0s 3ms/step - loss: 0.0115
Epoch 48/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0108
Epoch 49/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0104
Epoch 50/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0102
Epoch 51/100
1/1 [==============================] - 0s 3ms/step - loss: 0.0102
Epoch 52/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0103
Epoch 53/100
1/1 [==============================] - 0s 3ms/step - loss: 0.0105
Epoch 54/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0107
Epoch 55/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0107
Epoch 56/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0107
Epoch 57/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0105
Epoch 58/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0102
Epoch 59/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0099
Epoch 60/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0095
Epoch 61/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0093
Epoch 62/100
1/1 [==============================] - 0s 3ms/step - loss: 0.0091
Epoch 63/100
1/1 [==============================] - 0s 5ms/step - loss: 0.0090
Epoch 64/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0089
Epoch 65/100
1/1 [==============================] - 0s 3ms/step - loss: 0.0089
Epoch 66/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0090
Epoch 67/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0090
Epoch 68/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0090
Epoch 69/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0090
Epoch 70/100
1/1 [==============================] - 0s 3ms/step - loss: 0.0089
Epoch 71/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0088
Epoch 72/100
1/1 [==============================] - 0s 5ms/step - loss: 0.0087
Epoch 73/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0087
Epoch 74/100
1/1 [==============================] - 0s 5ms/step - loss: 0.0086
Epoch 75/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0085
Epoch 76/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0085
Epoch 77/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0085
Epoch 78/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0085
Epoch 79/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0085
Epoch 80/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0085
Epoch 81/100
1/1 [==============================] - 0s 3ms/step - loss: 0.0085
Epoch 82/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0084
Epoch 83/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0084
Epoch 84/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0084
Epoch 85/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0083
Epoch 86/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0083
Epoch 87/100
1/1 [==============================] - 0s 5ms/step - loss: 0.0083
Epoch 88/100
1/1 [==============================] - 0s 5ms/step - loss: 0.0083
Epoch 89/100
1/1 [==============================] - 0s 5ms/step - loss: 0.0083
Epoch 90/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0082
Epoch 91/100
1/1 [==============================] - 0s 5ms/step - loss: 0.0082
Epoch 92/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0082
Epoch 93/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0082
Epoch 94/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0082
Epoch 95/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0082
Epoch 96/100
1/1 [==============================] - 0s 3ms/step - loss: 0.0082
Epoch 97/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0081
Epoch 98/100
1/1 [==============================] - 0s 5ms/step - loss: 0.0081
Epoch 99/100
1/1 [==============================] - 0s 3ms/step - loss: 0.0081
Epoch 100/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0081
1/1 [==============================] - 0s 442ms/step
1/1 [==============================] - 0s 11ms/step
1/1 [==============================] - 0s 12ms/step
1/1 [==============================] - 0s 11ms/step
1/1 [==============================] - 0s 12ms/step
1/1 [==============================] - 0s 12ms/step
1/1 [==============================] - 0s 11ms/step
1/1 [==============================] - 0s 11ms/step
1/1 [==============================] - 0s 13ms/step
1/1 [==============================] - 0s 11ms/step

Epoch 1/100
1/1 [==============================] - 2s 2s/step - loss: 0.6695
Epoch 2/100
1/1 [==============================] - 0s 4ms/step - loss: 0.6249
Epoch 3/100
1/1 [==============================] - 0s 3ms/step - loss: 0.5816
Epoch 4/100
1/1 [==============================] - 0s 4ms/step - loss: 0.5395
Epoch 5/100
1/1 [==============================] - 0s 4ms/step - loss: 0.4982
Epoch 6/100
1/1 [==============================] - 0s 3ms/step - loss: 0.4576
Epoch 7/100
1/1 [==============================] - 0s 4ms/step - loss: 0.4176
Epoch 8/100
1/1 [==============================] - 0s 4ms/step - loss: 0.3779
Epoch 9/100
1/1 [==============================] - 0s 5ms/step - loss: 0.3386
Epoch 10/100
1/1 [==============================] - 0s 4ms/step - loss: 0.2995
Epoch 11/100
1/1 [==============================] - 0s 4ms/step - loss: 0.2608
Epoch 12/100
1/1 [==============================] - 0s 5ms/step - loss: 0.2228
Epoch 13/100
1/1 [==============================] - 0s 4ms/step - loss: 0.1858
Epoch 14/100
1/1 [==============================] - 0s 4ms/step - loss: 0.1505
Epoch 15/100
1/1 [==============================] - 0s 3ms/step - loss: 0.1176
Epoch 16/100
1/1 [==============================] - 0s 5ms/step - loss: 0.0883
Epoch 17/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0639
Epoch 18/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0458
Epoch 19/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0358
Epoch 20/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0346
Epoch 21/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0417
Epoch 22/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0539
Epoch 23/100
1/1 [==============================] - 0s 5ms/step - loss: 0.0658
Epoch 24/100
1/1 [==============================] - 0s 3ms/step - loss: 0.0731
Epoch 25/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0741
Epoch 26/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0694
Epoch 27/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0612
Epoch 28/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0519
Epoch 29/100
1/1 [==============================] - 0s 5ms/step - loss: 0.0433
Epoch 30/100
1/1 [==============================] - 0s 5ms/step - loss: 0.0364
Epoch 31/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0318
Epoch 32/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0294
Epoch 33/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0287
Epoch 34/100
1/1 [==============================] - 0s 5ms/step - loss: 0.0293
Epoch 35/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0305
Epoch 36/100
1/1 [==============================] - 0s 5ms/step - loss: 0.0320
Epoch 37/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0333
Epoch 38/100
1/1 [==============================] - 0s 5ms/step - loss: 0.0343
Epoch 39/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0348
Epoch 40/100
1/1 [==============================] - 0s 5ms/step - loss: 0.0347
Epoch 41/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0341
Epoch 42/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0331
Epoch 43/100
1/1 [==============================] - 0s 3ms/step - loss: 0.0318
Epoch 44/100
1/1 [==============================] - 0s 5ms/step - loss: 0.0303
Epoch 45/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0288
Epoch 46/100
1/1 [==============================] - 0s 5ms/step - loss: 0.0274
Epoch 47/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0262
Epoch 48/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0253
Epoch 49/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0247
Epoch 50/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0244
Epoch 51/100
1/1 [==============================] - 0s 5ms/step - loss: 0.0244
Epoch 52/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0245
Epoch 53/100
1/1 [==============================] - 0s 5ms/step - loss: 0.0247
Epoch 54/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0249
Epoch 55/100
1/1 [==============================] - 0s 3ms/step - loss: 0.0250
Epoch 56/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0249
Epoch 57/100
1/1 [==============================] - 0s 6ms/step - loss: 0.0247
Epoch 58/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0244
Epoch 59/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0240
Epoch 60/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0235
Epoch 61/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0231
Epoch 62/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0227
Epoch 63/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0224
Epoch 64/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0222
Epoch 65/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0221
Epoch 66/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0220
Epoch 67/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0219
Epoch 68/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0219
Epoch 69/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0218
Epoch 70/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0218
Epoch 71/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0217
Epoch 72/100
1/1 [==============================] - 0s 5ms/step - loss: 0.0216
Epoch 73/100
1/1 [==============================] - 0s 6ms/step - loss: 0.0214
Epoch 74/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0212
Epoch 75/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0211
Epoch 76/100
1/1 [==============================] - 0s 5ms/step - loss: 0.0209
Epoch 77/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0208
Epoch 78/100
1/1 [==============================] - 0s 5ms/step - loss: 0.0206
Epoch 79/100
1/1 [==============================] - 0s 5ms/step - loss: 0.0205
Epoch 80/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0204
Epoch 81/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0203
Epoch 82/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0202
Epoch 83/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0202
Epoch 84/100
1/1 [==============================] - 0s 5ms/step - loss: 0.0201
Epoch 85/100
1/1 [==============================] - 0s 5ms/step - loss: 0.0200
Epoch 86/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0199
Epoch 87/100
1/1 [==============================] - 0s 5ms/step - loss: 0.0198
Epoch 88/100
1/1 [==============================] - 0s 3ms/step - loss: 0.0198
Epoch 89/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0197
Epoch 90/100
1/1 [==============================] - 0s 3ms/step - loss: 0.0196
Epoch 91/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0195
Epoch 92/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0194
Epoch 93/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0193
Epoch 94/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0192
Epoch 95/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0192
Epoch 96/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0191
Epoch 97/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0190
Epoch 98/100
1/1 [==============================] - 0s 5ms/step - loss: 0.0190
Epoch 99/100
1/1 [==============================] - 0s 3ms/step - loss: 0.0189
Epoch 100/100
1/1 [==============================] - 0s 5ms/step - loss: 0.0188
1/1 [==============================] - 0s 443ms/step
1/1 [==============================] - 0s 12ms/step
1/1 [==============================] - 0s 12ms/step
1/1 [==============================] - 0s 11ms/step
1/1 [==============================] - 0s 13ms/step
1/1 [==============================] - 0s 12ms/step
1/1 [==============================] - 0s 12ms/step
1/1 [==============================] - 0s 13ms/step
1/1 [==============================] - 0s 12ms/step
1/1 [==============================] - 0s 13ms/step

Epoch 1/100
1/1 [==============================] - 3s 3s/step - loss: 0.3368
Epoch 2/100
1/1 [==============================] - 0s 4ms/step - loss: 0.3159
Epoch 3/100
1/1 [==============================] - 0s 4ms/step - loss: 0.2954
Epoch 4/100
1/1 [==============================] - 0s 4ms/step - loss: 0.2753
Epoch 5/100
1/1 [==============================] - 0s 4ms/step - loss: 0.2555
Epoch 6/100
1/1 [==============================] - 0s 4ms/step - loss: 0.2359
Epoch 7/100
1/1 [==============================] - 0s 3ms/step - loss: 0.2165
Epoch 8/100
1/1 [==============================] - 0s 4ms/step - loss: 0.1974
Epoch 9/100
1/1 [==============================] - 0s 3ms/step - loss: 0.1786
Epoch 10/100
1/1 [==============================] - 0s 4ms/step - loss: 0.1603
Epoch 11/100
1/1 [==============================] - 0s 4ms/step - loss: 0.1425
Epoch 12/100
1/1 [==============================] - 0s 4ms/step - loss: 0.1258
Epoch 13/100
1/1 [==============================] - 0s 5ms/step - loss: 0.1105
Epoch 14/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0971
Epoch 15/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0864
Epoch 16/100
1/1 [==============================] - 0s 3ms/step - loss: 0.0791
Epoch 17/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0759
Epoch 18/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0770
Epoch 19/100
1/1 [==============================] - 0s 3ms/step - loss: 0.0818
Epoch 20/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0879
Epoch 21/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0927
Epoch 22/100
1/1 [==============================] - 0s 3ms/step - loss: 0.0947
Epoch 23/100
1/1 [==============================] - 0s 5ms/step - loss: 0.0936
Epoch 24/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0903
Epoch 25/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0860
Epoch 26/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0817
Epoch 27/100
1/1 [==============================] - 0s 3ms/step - loss: 0.0781
Epoch 28/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0756
Epoch 29/100
1/1 [==============================] - 0s 5ms/step - loss: 0.0741
Epoch 30/100
1/1 [==============================] - 0s 5ms/step - loss: 0.0735
Epoch 31/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0737
Epoch 32/100
1/1 [==============================] - 0s 5ms/step - loss: 0.0742
Epoch 33/100
1/1 [==============================] - 0s 3ms/step - loss: 0.0749
Epoch 34/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0756
Epoch 35/100
1/1 [==============================] - 0s 5ms/step - loss: 0.0761
Epoch 36/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0764
Epoch 37/100
1/1 [==============================] - 0s 3ms/step - loss: 0.0765
Epoch 38/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0763
Epoch 39/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0758
Epoch 40/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0752
Epoch 41/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0745
Epoch 42/100
1/1 [==============================] - 0s 5ms/step - loss: 0.0738
Epoch 43/100
1/1 [==============================] - 0s 3ms/step - loss: 0.0731
Epoch 44/100
1/1 [==============================] - 0s 5ms/step - loss: 0.0725
Epoch 45/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0720
Epoch 46/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0717
Epoch 47/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0716
Epoch 48/100
1/1 [==============================] - 0s 5ms/step - loss: 0.0716
Epoch 49/100
1/1 [==============================] - 0s 5ms/step - loss: 0.0716
Epoch 50/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0717
Epoch 51/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0718
Epoch 52/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0718
Epoch 53/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0718
Epoch 54/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0716
Epoch 55/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0714
Epoch 56/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0712
Epoch 57/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0710
Epoch 58/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0707
Epoch 59/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0705
Epoch 60/100
1/1 [==============================] - 0s 5ms/step - loss: 0.0704
Epoch 61/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0703
Epoch 62/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0702
Epoch 63/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0701
Epoch 64/100
1/1 [==============================] - 0s 3ms/step - loss: 0.0701
Epoch 65/100
1/1 [==============================] - 0s 5ms/step - loss: 0.0700
Epoch 66/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0700
Epoch 67/100
1/1 [==============================] - 0s 5ms/step - loss: 0.0699
Epoch 68/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0698
Epoch 69/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0697
Epoch 70/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0696
Epoch 71/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0695
Epoch 72/100
1/1 [==============================] - 0s 5ms/step - loss: 0.0693
Epoch 73/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0692
Epoch 74/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0691
Epoch 75/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0690
Epoch 76/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0689
Epoch 77/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0688
Epoch 78/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0688
Epoch 79/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0687
Epoch 80/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0686
Epoch 81/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0685
Epoch 82/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0684
Epoch 83/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0683
Epoch 84/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0682
Epoch 85/100
1/1 [==============================] - 0s 5ms/step - loss: 0.0681
Epoch 86/100
1/1 [==============================] - 0s 3ms/step - loss: 0.0680
Epoch 87/100
1/1 [==============================] - 0s 5ms/step - loss: 0.0679
Epoch 88/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0678
Epoch 89/100
1/1 [==============================] - 0s 3ms/step - loss: 0.0677
Epoch 90/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0676
Epoch 91/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0675
Epoch 92/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0674
Epoch 93/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0673
Epoch 94/100
1/1 [==============================] - 0s 5ms/step - loss: 0.0672
Epoch 95/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0671
Epoch 96/100
1/1 [==============================] - 0s 5ms/step - loss: 0.0670
Epoch 97/100
1/1 [==============================] - 0s 3ms/step - loss: 0.0669
Epoch 98/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0667
Epoch 99/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0666
Epoch 100/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0665
1/1 [==============================] - 0s 445ms/step
1/1 [==============================] - 0s 11ms/step
1/1 [==============================] - 0s 12ms/step
1/1 [==============================] - 0s 11ms/step
1/1 [==============================] - 0s 12ms/step
1/1 [==============================] - 0s 12ms/step
1/1 [==============================] - 0s 12ms/step
1/1 [==============================] - 0s 12ms/step
1/1 [==============================] - 0s 12ms/step
1/1 [==============================] - 0s 11ms/step

Epoch 1/100
1/1 [==============================] - 2s 2s/step - loss: 0.3305
Epoch 2/100
1/1 [==============================] - 0s 5ms/step - loss: 0.3088
Epoch 3/100
1/1 [==============================] - 0s 4ms/step - loss: 0.2876
Epoch 4/100
1/1 [==============================] - 0s 3ms/step - loss: 0.2668
Epoch 5/100
1/1 [==============================] - 0s 4ms/step - loss: 0.2461
Epoch 6/100
1/1 [==============================] - 0s 4ms/step - loss: 0.2254
Epoch 7/100
1/1 [==============================] - 0s 3ms/step - loss: 0.2046
Epoch 8/100
1/1 [==============================] - 0s 4ms/step - loss: 0.1836
Epoch 9/100
1/1 [==============================] - 0s 5ms/step - loss: 0.1625
Epoch 10/100
1/1 [==============================] - 0s 4ms/step - loss: 0.1414
Epoch 11/100
1/1 [==============================] - 0s 5ms/step - loss: 0.1205
Epoch 12/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0999
Epoch 13/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0801
Epoch 14/100
1/1 [==============================] - 0s 5ms/step - loss: 0.0615
Epoch 15/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0446
Epoch 16/100
1/1 [==============================] - 0s 3ms/step - loss: 0.0303
Epoch 17/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0194
Epoch 18/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0128
Epoch 19/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0113
Epoch 20/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0148
Epoch 21/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0219
Epoch 22/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0294
Epoch 23/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0343
Epoch 24/100
1/1 [==============================] - 0s 3ms/step - loss: 0.0351
Epoch 25/100
1/1 [==============================] - 0s 3ms/step - loss: 0.0322
Epoch 26/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0272
Epoch 27/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0214
Epoch 28/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0162
Epoch 29/100
1/1 [==============================] - 0s 5ms/step - loss: 0.0123
Epoch 30/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0099
Epoch 31/100
1/1 [==============================] - 0s 3ms/step - loss: 0.0088
Epoch 32/100
1/1 [==============================] - 0s 5ms/step - loss: 0.0088
Epoch 33/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0095
Epoch 34/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0104
Epoch 35/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0114
Epoch 36/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0122
Epoch 37/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0126
Epoch 38/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0126
Epoch 39/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0122
Epoch 40/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0114
Epoch 41/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0104
Epoch 42/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0092
Epoch 43/100
1/1 [==============================] - 0s 5ms/step - loss: 0.0081
Epoch 44/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0069
Epoch 45/100
1/1 [==============================] - 0s 3ms/step - loss: 0.0060
Epoch 46/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0053
Epoch 47/100
1/1 [==============================] - 0s 3ms/step - loss: 0.0050
Epoch 48/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0048
Epoch 49/100
1/1 [==============================] - 0s 5ms/step - loss: 0.0049
Epoch 50/100
1/1 [==============================] - 0s 5ms/step - loss: 0.0050
Epoch 51/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0052
Epoch 52/100
1/1 [==============================] - 0s 3ms/step - loss: 0.0052
Epoch 53/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0051
Epoch 54/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0048
Epoch 55/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0044
Epoch 56/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0039
Epoch 57/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0034
Epoch 58/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0031
Epoch 59/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0028
Epoch 60/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0026
Epoch 61/100
1/1 [==============================] - 0s 3ms/step - loss: 0.0025
Epoch 62/100
1/1 [==============================] - 0s 5ms/step - loss: 0.0025
Epoch 63/100
1/1 [==============================] - 0s 3ms/step - loss: 0.0025
Epoch 64/100
1/1 [==============================] - 0s 5ms/step - loss: 0.0025
Epoch 65/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0024
Epoch 66/100
1/1 [==============================] - 0s 3ms/step - loss: 0.0023
Epoch 67/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0022
Epoch 68/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0020
Epoch 69/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0019
Epoch 70/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0017
Epoch 71/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0016
Epoch 72/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0016
Epoch 73/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0015
Epoch 74/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0015
Epoch 75/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0015
Epoch 76/100
1/1 [==============================] - 0s 3ms/step - loss: 0.0015
Epoch 77/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0015
Epoch 78/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0015
Epoch 79/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0014
Epoch 80/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0014
Epoch 81/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0013
Epoch 82/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0013
Epoch 83/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0013
Epoch 84/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0013
Epoch 85/100
1/1 [==============================] - 0s 5ms/step - loss: 0.0013
Epoch 86/100
1/1 [==============================] - 0s 3ms/step - loss: 0.0013
Epoch 87/100
1/1 [==============================] - 0s 3ms/step - loss: 0.0013
Epoch 88/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0013
Epoch 89/100
1/1 [==============================] - 0s 3ms/step - loss: 0.0013
Epoch 90/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0013
Epoch 91/100
1/1 [==============================] - 0s 5ms/step - loss: 0.0013
Epoch 92/100
1/1 [==============================] - 0s 5ms/step - loss: 0.0013
Epoch 93/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0013
Epoch 94/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0013
Epoch 95/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0013
Epoch 96/100
1/1 [==============================] - 0s 3ms/step - loss: 0.0013
Epoch 97/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0013
Epoch 98/100
1/1 [==============================] - 0s 5ms/step - loss: 0.0013
Epoch 99/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0013
Epoch 100/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0013
1/1 [==============================] - 0s 438ms/step
1/1 [==============================] - 0s 12ms/step
1/1 [==============================] - 0s 11ms/step
1/1 [==============================] - 0s 12ms/step
1/1 [==============================] - 0s 11ms/step
1/1 [==============================] - 0s 12ms/step
1/1 [==============================] - 0s 11ms/step
1/1 [==============================] - 0s 13ms/step
1/1 [==============================] - 0s 14ms/step
1/1 [==============================] - 0s 13ms/step

Epoch 1/100
1/1 [==============================] - 2s 2s/step - loss: 0.4490
Epoch 2/100
1/1 [==============================] - 0s 4ms/step - loss: 0.4158
Epoch 3/100
1/1 [==============================] - 0s 4ms/step - loss: 0.3837
Epoch 4/100
1/1 [==============================] - 0s 4ms/step - loss: 0.3522
Epoch 5/100
1/1 [==============================] - 0s 4ms/step - loss: 0.3212
Epoch 6/100
1/1 [==============================] - 0s 4ms/step - loss: 0.2905
Epoch 7/100
1/1 [==============================] - 0s 4ms/step - loss: 0.2600
Epoch 8/100
1/1 [==============================] - 0s 4ms/step - loss: 0.2297
Epoch 9/100
1/1 [==============================] - 0s 3ms/step - loss: 0.1997
Epoch 10/100
1/1 [==============================] - 0s 4ms/step - loss: 0.1700
Epoch 11/100
1/1 [==============================] - 0s 4ms/step - loss: 0.1408
Epoch 12/100
1/1 [==============================] - 0s 4ms/step - loss: 0.1125
Epoch 13/100
1/1 [==============================] - 0s 5ms/step - loss: 0.0855
Epoch 14/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0606
Epoch 15/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0386
Epoch 16/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0206
Epoch 17/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0080
Epoch 18/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0018
Epoch 19/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0029
Epoch 20/100
1/1 [==============================] - 0s 3ms/step - loss: 0.0103
Epoch 21/100
1/1 [==============================] - 0s 3ms/step - loss: 0.0210
Epoch 22/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0305
Epoch 23/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0352
Epoch 24/100
1/1 [==============================] - 0s 3ms/step - loss: 0.0345
Epoch 25/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0295
Epoch 26/100
1/1 [==============================] - 0s 5ms/step - loss: 0.0225
Epoch 27/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0152
Epoch 28/100
1/1 [==============================] - 0s 5ms/step - loss: 0.0090
Epoch 29/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0047
Epoch 30/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0022
Epoch 31/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0015
Epoch 32/100
1/1 [==============================] - 0s 5ms/step - loss: 0.0020
Epoch 33/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0032
Epoch 34/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0047
Epoch 35/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0062
Epoch 36/100
1/1 [==============================] - 0s 5ms/step - loss: 0.0074
Epoch 37/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0082
Epoch 38/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0085
Epoch 39/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0082
Epoch 40/100
1/1 [==============================] - 0s 5ms/step - loss: 0.0076
Epoch 41/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0067
Epoch 42/100
1/1 [==============================] - 0s 5ms/step - loss: 0.0056
Epoch 43/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0044
Epoch 44/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0033
Epoch 45/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0024
Epoch 46/100
1/1 [==============================] - 0s 5ms/step - loss: 0.0018
Epoch 47/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0015
Epoch 48/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0014
Epoch 49/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0016
Epoch 50/100
1/1 [==============================] - 0s 5ms/step - loss: 0.0020
Epoch 51/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0023
Epoch 52/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0026
Epoch 53/100
1/1 [==============================] - 0s 5ms/step - loss: 0.0028
Epoch 54/100
1/1 [==============================] - 0s 6ms/step - loss: 0.0028
Epoch 55/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0026
Epoch 56/100
1/1 [==============================] - 0s 3ms/step - loss: 0.0024
Epoch 57/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0021
Epoch 58/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0017
Epoch 59/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0015
Epoch 60/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0013
Epoch 61/100
1/1 [==============================] - 0s 3ms/step - loss: 0.0012
Epoch 62/100
1/1 [==============================] - 0s 5ms/step - loss: 0.0012
Epoch 63/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0013
Epoch 64/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0013
Epoch 65/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0014
Epoch 66/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0015
Epoch 67/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0015
Epoch 68/100
1/1 [==============================] - 0s 5ms/step - loss: 0.0015
Epoch 69/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0014
Epoch 70/100
1/1 [==============================] - 0s 5ms/step - loss: 0.0013
Epoch 71/100
1/1 [==============================] - 0s 5ms/step - loss: 0.0013
Epoch 72/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0012
Epoch 73/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0011
Epoch 74/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0011
Epoch 75/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0010
Epoch 76/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0010
Epoch 77/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0010
Epoch 78/100
1/1 [==============================] - 0s 5ms/step - loss: 0.0011
Epoch 79/100
1/1 [==============================] - 0s 5ms/step - loss: 0.0011
Epoch 80/100
1/1 [==============================] - 0s 3ms/step - loss: 0.0011
Epoch 81/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0011
Epoch 82/100
1/1 [==============================] - 0s 5ms/step - loss: 0.0011
Epoch 83/100
1/1 [==============================] - 0s 4ms/step - loss: 0.0010
Epoch 84/100
1/1 [==============================] - 0s 5ms/step - loss: 0.0010
Epoch 85/100
1/1 [==============================] - 0s 4ms/step - loss: 9.7290e-04
Epoch 86/100
1/1 [==============================] - 0s 4ms/step - loss: 9.5115e-04
Epoch 87/100
1/1 [==============================] - 0s 5ms/step - loss: 9.3697e-04
Epoch 88/100
1/1 [==============================] - 0s 3ms/step - loss: 9.3026e-04
Epoch 89/100
1/1 [==============================] - 0s 4ms/step - loss: 9.2932e-04
Epoch 90/100
1/1 [==============================] - 0s 4ms/step - loss: 9.3154e-04
Epoch 91/100
1/1 [==============================] - 0s 5ms/step - loss: 9.3416e-04
Epoch 92/100
1/1 [==============================] - 0s 5ms/step - loss: 9.3492e-04
Epoch 93/100
1/1 [==============================] - 0s 5ms/step - loss: 9.3252e-04
Epoch 94/100
1/1 [==============================] - 0s 5ms/step - loss: 9.2672e-04
Epoch 95/100
1/1 [==============================] - 0s 5ms/step - loss: 9.1824e-04
Epoch 96/100
1/1 [==============================] - 0s 5ms/step - loss: 9.0841e-04
Epoch 97/100
1/1 [==============================] - 0s 4ms/step - loss: 8.9875e-04
Epoch 98/100
1/1 [==============================] - 0s 5ms/step - loss: 8.9057e-04
Epoch 99/100
1/1 [==============================] - 0s 5ms/step - loss: 8.8466e-04
Epoch 100/100
1/1 [==============================] - 0s 4ms/step - loss: 8.8120e-04
1/1 [==============================] - 0s 447ms/step
1/1 [==============================] - 0s 12ms/step
1/1 [==============================] - 0s 11ms/step
1/1 [==============================] - 0s 11ms/step
1/1 [==============================] - 0s 11ms/step
1/1 [==============================] - 0s 12ms/step
1/1 [==============================] - 0s 12ms/step
1/1 [==============================] - 0s 12ms/step
1/1 [==============================] - 0s 11ms/step
1/1 [==============================] - 0s 11ms/step
Out[15]:
year Total Energy Consumption(10000 tons of SCE) Proportion of Coal(%) Proportion of Petroleum(%) Proportion of Natural Gas(%) Proportion of Primary Electricity and Other Energy(%) Consumption of Coal(10000 tons) Consumption of Coke(10000 tons) Consumption of Crude Oil(10000 tons) Consumption of Gasoline(10000 tons) Consumption of Kerosene(10000 tons) Consumption of Diesel Oil(10000 tons) Consumption of Fuel Oil(10000 tons) Consumption of Natural Gas(100 million cu.m) Consumption of Electricity(100 million kwh)
0 2003 197083.0000 70.200000 20.100000 2.300000 7.400000 183760.24000 15926.470000 25180.720000 4118.520000 921.610000 8575.120000 4330.340000 339.080000 19031.600000
1 2004 230281.0000 70.200000 19.900000 2.300000 7.600000 212161.83000 18067.010000 29009.310000 4695.720000 1060.860000 10206.920000 4844.760000 396.720000 21971.370000
2 2005 261369.0000 72.400000 17.800000 2.400000 7.400000 243375.44000 25105.840000 30088.940000 4854.910000 1076.840000 10974.940000 4244.160000 466.080000 24940.320000
3 2006 286467.0000 72.400000 17.500000 2.700000 7.400000 270639.45000 28297.760000 32245.200000 5242.550000 1124.740000 11729.090000 4471.150000 573.320000 28587.970000
4 2007 311442.0000 72.500000 17.000000 3.000000 7.500000 290410.12000 31168.120000 34031.600000 5519.090000 1243.720000 12492.380000 4157.490000 705.230000 32711.810000
5 2008 320611.0000 71.500000 16.700000 3.400000 8.400000 300604.94000 32120.240000 35510.340000 6145.520000 1294.010000 13544.940000 3236.750000 812.940000 34541.350000
6 2009 336126.0000 71.600000 16.400000 3.500000 8.500000 325002.93000 36349.970000 38128.590000 6172.690000 1450.490000 13551.430000 2828.800000 895.200000 37032.140000
7 2010 360648.0000 69.200000 17.400000 4.000000 9.400000 349008.26000 38702.790000 42874.550000 6956.200000 1765.170000 14699.000000 3758.020000 1080.240000 41934.490000
8 2011 387043.0000 70.200000 16.800000 4.600000 8.400000 388961.10000 42063.280000 43965.840000 7595.950000 1816.720000 15635.100000 3662.800000 1341.070000 47000.880000
9 2012 402138.0000 68.500000 17.000000 4.800000 9.700000 411726.90000 44805.230000 46678.920000 8165.900000 1956.600000 16966.040000 3683.280000 1497.000000 49762.640000
10 2013 416913.0000 67.400000 17.100000 5.300000 10.200000 424425.94000 45851.870000 48652.150000 9366.350000 2164.070000 17150.650000 3953.970000 1705.370000 54203.410000
11 2014 428334.0000 65.800000 17.300000 5.600000 11.300000 413633.00000 46885.000000 51596.950000 9776.370000 2335.420000 17165.290000 4355.470000 1870.630000 57829.690000
12 2015 434113.0000 63.800000 18.400000 5.800000 12.000000 399834.00000 44059.000000 54788.280000 11368.460000 2663.710000 17360.310000 4662.010000 1931.750000 58019.980000
13 2016 441492.0000 62.200000 18.700000 6.100000 13.000000 388820.00000 45462.000000 57125.930000 11866.040000 2970.710000 16839.040000 4631.040000 2078.060000 61205.090000
14 2017 455827.0000 60.600000 18.900000 6.900000 13.600000 391403.00000 43743.000000 59402.170000 12296.270000 3326.360000 16916.540000 4887.300000 2393.690000 65913.970000
15 2018 471925.0000 59.000000 18.900000 7.600000 14.500000 397452.00000 43717.000000 63004.330000 13055.300000 3653.510000 16409.560000 4536.070000 2817.090000 71508.200000
16 2019 487488.0000 57.700000 19.000000 8.000000 15.300000 401915.00000 46426.000000 67268.270000 13627.970000 3950.230000 14917.950000 4690.340000 3059.680000 74866.120000
17 2020 498314.0000 56.900000 18.800000 8.400000 15.900000 404860.00000 48310.000000 69477.140000 12767.160000 3352.100000 14282.700000 5364.600000 3339.890000 77620.170000
18 2021 524000.0000 56.000000 18.500000 8.900000 16.600000 409987.12500 47249.488281 73206.304688 14802.031250 4382.992188 16466.960938 4521.073730 3742.249756 81274.703125
19 2022 541000.0000 56.200000 18.124666 9.945584 18.330135 412244.09375 47433.878906 76895.078125 15292.958984 4678.936035 16311.519531 4528.297852 4254.971680 85783.210938
20 2023 534874.6250 53.667168 17.976780 10.689278 19.428606 414670.65625 47985.304688 81011.765625 15839.913086 4944.760742 16168.312500 4498.509277 4804.278320 90022.226562
21 2024 547432.0000 53.061035 17.849726 11.412879 20.565023 416650.18750 48621.925781 85112.640625 16345.994141 5192.088867 16108.673828 4489.799316 5354.038086 93704.507812
22 2025 558753.8125 52.537197 17.819319 12.261145 21.793034 418353.25000 48882.558594 88971.835938 16858.433594 5432.677734 16262.715820 4463.012695 6012.355957 97516.695312
23 2026 569917.7500 52.032959 17.845015 13.257030 23.203493 419957.25000 48883.785156 93389.843750 17774.503906 5986.672363 16514.144531 4379.600098 6782.358398 101655.750000
24 2027 577079.3750 51.581459 17.897102 14.378264 24.767618 421070.65625 49099.863281 97851.554688 18300.417969 6290.188477 16495.296875 4367.132324 7619.170410 105853.789062
25 2028 582170.3125 51.045490 17.934679 15.406851 26.110659 422082.78125 49327.128906 102395.484375 18851.275391 6592.424316 16505.386719 4351.123047 8477.360352 109846.609375
26 2029 590807.4375 50.797585 17.973047 16.475143 27.463623 422931.25000 49500.972656 106884.000000 19407.974609 6902.262695 16541.410156 4336.054199 9342.809570 113704.679688
27 2030 598347.6250 50.580982 17.995325 17.596319 28.809925 423651.90625 49601.191406 111330.640625 19977.380859 7224.102539 16591.585938 4320.798828 10213.050781 117583.570312
28 2031 604975.3750 50.393036 18.004433 18.722374 30.114101 NaN NaN NaN NaN NaN NaN NaN NaN NaN
29 2032 610608.6875 50.237183 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN
In [16]:
from prophet import Prophet

# Define the list of columns to forecast
cols = ["Total Energy Consumption, Agriculture, Forestry, Animal Husbandry and Fishery(10000 tons of SCE)", 
        "Total Energy Consumption, Manufacturing(10000 tons of SCE)", 
        "Total Energy Consumption, Manufacture of Computers, Communication and Other Electronic Equipment(10000 tons of SCE)",
        "Total Energy Consumption, Wholesale, Retail Trade and Hotel, Restaurants(10000 tons of SCE)"]

Prophet_Results = []

for col in cols:
    # Load data
    target_column = col
    df = pd.read_excel("D:\Jupyter Directory\Dissertation\Dissertation Datasets\Ones I will use\Consumption by Sector.xls")

    # Preprocessing: Set the index, select relevant columns, and rename them

    df.set_index("Database：Annual", inplace=True)

    # Prepare the data
    new_df = df.T[["Indicators", target_column]].reset_index(drop=True)
    new_df["Indicators"] =new_df["Indicators"].astype("int")
    new_df["date"] = pd.to_datetime(new_df["Indicators"].astype(str) + "-12-31")

    new_df = new_df.rename(columns={"date": "ds", target_column: "y"}).sort_values(by="ds")
    new_df.dropna(inplace=True)

    # Create and fit the Prophet model
    model = Prophet()
    model.fit(new_df)

    # Make future dataframe for forecasting
    future = model.make_future_dataframe(periods=10, freq="Y")

    # Forecast the next 10 data points
    forecast = model.predict(future)

    # Append the forecasted values to the original data
    extended_t = future["ds"]
    extended_y = np.concatenate([new_df["y"].values, forecast["yhat"][-10:]])

    Result = pd.DataFrame({"Year": extended_t, target_column: extended_y})

    Result["Year"] = Result["Year"].dt.year

    Prophet_Results.append(Result)

    # Plot original data and forecasted values
    plt.figure(figsize=(10, 6))
    plt.plot(new_df["ds"], new_df["y"], color="dodgerblue", linewidth=2, label="Original Data")
    plt.plot(forecast["ds"][-10:], forecast["yhat"][-10:], linestyle="dashed", color="darkorange", label="Forecasted Values")
    plt.fill_between(forecast["ds"][-10:], forecast["yhat_lower"][-10:], forecast["yhat_upper"][-10:], color="darkorange", alpha=0.1)
    plt.xlabel("Year")
    plt.ylabel(target_column)
    plt.title("Prophet Forecast")
    plt.grid(True)
    plt.legend()
    plt.show()
    
Final_Prophet_Results1 = pd.concat(Prophet_Results, axis=1)

Final_Prophet_Results1.columns.values[0] = "year"

# Assuming 'df' is your DataFrame
cols_to_drop = [col for col in Final_Prophet_Results1.columns if col == "Year"][1:]
Final_Prophet_Results1.drop(columns=cols_to_drop, inplace=True)

Final_Prophet_Results1

23:55:13 - cmdstanpy - INFO - Chain [1] start processing
23:55:13 - cmdstanpy - INFO - Chain [1] done processing

23:55:13 - cmdstanpy - INFO - Chain [1] start processing
23:55:13 - cmdstanpy - INFO - Chain [1] done processing

23:55:13 - cmdstanpy - INFO - Chain [1] start processing
23:55:13 - cmdstanpy - INFO - Chain [1] done processing

23:55:14 - cmdstanpy - INFO - Chain [1] start processing
23:55:14 - cmdstanpy - INFO - Chain [1] done processing
Out[16]:
year Total Energy Consumption, Agriculture, Forestry, Animal Husbandry and Fishery(10000 tons of SCE) Total Energy Consumption, Manufacturing(10000 tons of SCE) Total Energy Consumption, Manufacture of Computers, Communication and Other Electronic Equipment(10000 tons of SCE) Total Energy Consumption, Wholesale, Retail Trade and Hotel, Restaurants(10000 tons of SCE)
0 2003 5683.210000 111222.870000 1098.680000 4723.400000
1 2004 6391.860000 136407.850000 1316.210000 5498.790000
2 2005 6860.460000 158234.920000 1517.970000 5916.630000
3 2006 7153.520000 174920.090000 1886.510000 6358.180000
4 2007 7068.450000 193133.070000 2096.360000 6731.960000
5 2008 6872.630000 198406.180000 2219.950000 6884.910000
6 2009 6978.210000 206555.600000 2228.680000 7303.220000
7 2010 7266.500000 217328.870000 2547.390000 7847.100000
8 2011 7675.230000 229090.990000 2650.410000 9147.500000
9 2012 7803.570000 234538.810000 2689.440000 10012.330000
10 2013 8054.800000 239053.400000 2801.590000 10598.160000
11 2014 8020.000000 248976.000000 2968.000000 10864.000000
12 2015 8271.000000 248264.000000 3149.000000 11447.000000
13 2016 8585.000000 247658.000000 3377.000000 12042.000000
14 2017 8945.000000 252462.000000 3662.000000 12456.000000
15 2018 8781.000000 258604.000000 4628.000000 12994.000000
16 2019 9018.000000 268426.000000 5028.000000 13624.000000
17 2020 9263.000000 279651.000000 5120.000000 13171.000000
18 2021 9483.672433 279005.864547 4824.790275 14490.469982
19 2022 9640.351806 284063.010650 5080.972749 15080.286198
20 2023 9723.883430 288983.878013 5367.006228 15694.228230
21 2024 9980.283956 294467.337543 5453.784355 16119.260677
22 2025 10210.157250 299659.728062 5680.056562 16684.926141
23 2026 10366.836623 304716.874166 5936.239036 17274.742357
24 2027 10450.368247 309637.741529 6222.272515 17888.684389
25 2028 10706.768773 315121.201059 6309.050642 18313.716837
26 2029 10936.642067 320313.591578 6535.322849 18879.382300
27 2030 11093.321440 325370.737682 6791.505323 19469.198517
In [17]:
from prophet import Prophet

# Define the list of columns to forecast
cols = ["Total Energy Consumption(10000 tons of SCE)",
        "Proportion of Coal(%)",
        "Proportion of Petroleum(%)", 
        "Proportion of Natural Gas(%)",
        "Proportion of Primary  Electricity and  Other Energy(%)",
        "Consumption of Coal(10000 tons)", 
        "Consumption of Coke(10000 tons)",
        "Consumption of Crude Oil(10000 tons)",
        "Consumption of Gasoline(10000 tons)",
        "Consumption of Kerosene(10000 tons)",
        "Consumption of Diesel Oil(10000 tons)",
        "Consumption of Fuel Oil(10000 tons)",
        "Consumption of Natural Gas(100 million cu.m)",
        "Consumption of Electricity(100 million kwh)"]

Prophet_Results = []

for col in cols:
    # Load data
    target_column = col
    df = pd.read_excel("D:\Jupyter Directory\Dissertation\Dissertation Datasets\Ones I will use\Annual Total Energy Consumption.xls")

    # Preprocessing: Set the index, select relevant columns, and rename them

    df.set_index("Database：Annual", inplace=True)

    # Prepare the data
    new_df = df.T[["Indicators", target_column]].reset_index(drop=True)
    new_df["Indicators"] =new_df["Indicators"].astype("int")
    new_df["date"] = pd.to_datetime(new_df["Indicators"].astype(str) + "-12-31")

    new_df = new_df.rename(columns={"date": "ds", target_column: "y"}).sort_values(by="ds")
    new_df.dropna(inplace=True)

    # Create and fit the Prophet model
    model = Prophet()
    model.fit(new_df)

    # Make future dataframe for forecasting
    future = model.make_future_dataframe(periods=10, freq="Y")

    # Forecast the next 10 data points
    forecast = model.predict(future)

    # Append the forecasted values to the original data
    extended_t = future["ds"]
    extended_y = np.concatenate([new_df["y"].values, forecast["yhat"][-10:]])

    Result = pd.DataFrame({"Year": extended_t, target_column: extended_y})

    Result["Year"] = Result["Year"].dt.year

    Prophet_Results.append(Result)

    # Plot original data and forecasted values
    plt.figure(figsize=(10, 6))
    plt.plot(new_df["ds"], new_df["y"], color="dodgerblue", linewidth=2, label="Original Data")
    plt.plot(forecast["ds"][-10:], forecast["yhat"][-10:], linestyle="dashed", color="darkorange", label="Forecasted Values")
    plt.fill_between(forecast["ds"][-10:], forecast["yhat_lower"][-10:], forecast["yhat_upper"][-10:], color="darkorange", alpha=0.1)
    plt.xlabel("Year")
    plt.ylabel(target_column)
    plt.title("Prophet Forecast")
    plt.grid(True)
    plt.legend()
    plt.show()
    
Final_Prophet_Results2= pd.concat(Prophet_Results, axis=1)

Final_Prophet_Results2.columns.values[0] = "year"

# Assuming 'df' is your DataFrame
cols_to_drop = [col for col in Final_Prophet_Results2.columns if col == "Year"][1:]
Final_Prophet_Results2.drop(columns=cols_to_drop, inplace=True)

Final_Prophet_Results2

23:55:14 - cmdstanpy - INFO - Chain [1] start processing
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Out[17]:
year Total Energy Consumption(10000 tons of SCE) Proportion of Coal(%) Proportion of Petroleum(%) Proportion of Natural Gas(%) Proportion of Primary Electricity and Other Energy(%) Consumption of Coal(10000 tons) Consumption of Coke(10000 tons) Consumption of Crude Oil(10000 tons) Consumption of Gasoline(10000 tons) Consumption of Kerosene(10000 tons) Consumption of Diesel Oil(10000 tons) Consumption of Fuel Oil(10000 tons) Consumption of Natural Gas(100 million cu.m) Consumption of Electricity(100 million kwh)
0 2003 197083.000000 70.200000 20.100000 2.300000 7.400000 183760.240000 15926.470000 25180.720000 4118.520000 921.610000 8575.120000 4330.340000 339.080000 19031.600000
1 2004 230281.000000 70.200000 19.900000 2.300000 7.600000 212161.830000 18067.010000 29009.310000 4695.720000 1060.860000 10206.920000 4844.760000 396.720000 21971.370000
2 2005 261369.000000 72.400000 17.800000 2.400000 7.400000 243375.440000 25105.840000 30088.940000 4854.910000 1076.840000 10974.940000 4244.160000 466.080000 24940.320000
3 2006 286467.000000 72.400000 17.500000 2.700000 7.400000 270639.450000 28297.760000 32245.200000 5242.550000 1124.740000 11729.090000 4471.150000 573.320000 28587.970000
4 2007 311442.000000 72.500000 17.000000 3.000000 7.500000 290410.120000 31168.120000 34031.600000 5519.090000 1243.720000 12492.380000 4157.490000 705.230000 32711.810000
5 2008 320611.000000 71.500000 16.700000 3.400000 8.400000 300604.940000 32120.240000 35510.340000 6145.520000 1294.010000 13544.940000 3236.750000 812.940000 34541.350000
6 2009 336126.000000 71.600000 16.400000 3.500000 8.500000 325002.930000 36349.970000 38128.590000 6172.690000 1450.490000 13551.430000 2828.800000 895.200000 37032.140000
7 2010 360648.000000 69.200000 17.400000 4.000000 9.400000 349008.260000 38702.790000 42874.550000 6956.200000 1765.170000 14699.000000 3758.020000 1080.240000 41934.490000
8 2011 387043.000000 70.200000 16.800000 4.600000 8.400000 388961.100000 42063.280000 43965.840000 7595.950000 1816.720000 15635.100000 3662.800000 1341.070000 47000.880000
9 2012 402138.000000 68.500000 17.000000 4.800000 9.700000 411726.900000 44805.230000 46678.920000 8165.900000 1956.600000 16966.040000 3683.280000 1497.000000 49762.640000
10 2013 416913.000000 67.400000 17.100000 5.300000 10.200000 424425.940000 45851.870000 48652.150000 9366.350000 2164.070000 17150.650000 3953.970000 1705.370000 54203.410000
11 2014 428334.000000 65.800000 17.300000 5.600000 11.300000 413633.000000 46885.000000 51596.950000 9776.370000 2335.420000 17165.290000 4355.470000 1870.630000 57829.690000
12 2015 434113.000000 63.800000 18.400000 5.800000 12.000000 399834.000000 44059.000000 54788.280000 11368.460000 2663.710000 17360.310000 4662.010000 1931.750000 58019.980000
13 2016 441492.000000 62.200000 18.700000 6.100000 13.000000 388820.000000 45462.000000 57125.930000 11866.040000 2970.710000 16839.040000 4631.040000 2078.060000 61205.090000
14 2017 455827.000000 60.600000 18.900000 6.900000 13.600000 391403.000000 43743.000000 59402.170000 12296.270000 3326.360000 16916.540000 4887.300000 2393.690000 65913.970000
15 2018 471925.000000 59.000000 18.900000 7.600000 14.500000 397452.000000 43717.000000 63004.330000 13055.300000 3653.510000 16409.560000 4536.070000 2817.090000 71508.200000
16 2019 487488.000000 57.700000 19.000000 8.000000 15.300000 401915.000000 46426.000000 67268.270000 13627.970000 3950.230000 14917.950000 4690.340000 3059.680000 74866.120000
17 2020 498314.000000 56.900000 18.800000 8.400000 15.900000 404860.000000 48310.000000 69477.140000 12767.160000 3352.100000 14282.700000 5364.600000 3339.890000 77620.170000
18 2021 524000.000000 56.000000 18.500000 8.900000 16.600000 465297.962695 54254.818304 72172.246077 14196.594002 3797.397907 18299.402048 4669.051809 3153.539418 81022.389067
19 2022 541000.000000 56.200000 19.164792 9.435511 17.399416 478402.023070 55948.450110 75255.216217 14898.577992 4032.950992 18641.232188 4736.370677 3339.750626 84910.813950
20 2023 555943.601985 54.275223 19.636072 10.000462 18.113629 488681.510792 57309.920985 78510.418625 15659.831879 4271.736685 18854.553854 4827.712475 3555.300952 88684.399181
21 2024 568674.817929 53.129654 19.802041 10.364465 19.053135 499724.231787 59149.022362 81294.569487 15974.065764 4284.845321 19422.761755 4816.734905 3696.375629 91299.311283
22 2025 588364.678969 52.615802 19.735367 10.893872 19.763673 515652.256433 61174.858907 84204.175306 16616.670506 4517.098724 19893.168770 4860.004103 3853.232038 95301.711116
23 2026 606527.487063 51.736206 19.937720 11.441089 20.476083 528756.316808 62868.490713 87287.145446 17318.654497 4752.651808 20234.998911 4927.322971 4039.443246 99190.135999
24 2027 623160.930674 50.491853 20.408999 12.006039 21.190296 539035.804530 64229.961588 90542.347854 18079.908384 4991.437501 20448.320577 5018.664769 4254.993572 102963.721230
25 2028 635892.146619 49.346283 20.574969 12.370043 22.129802 550078.525525 66069.062965 93326.498716 18394.142269 5004.546137 21016.528478 5007.687199 4396.068249 105578.633332
26 2029 655582.007658 48.832432 20.508295 12.899450 22.840340 566006.550171 68094.899510 96236.104535 19036.747011 5236.799540 21486.935493 5050.956397 4552.924658 109581.033165
27 2030 673744.815753 47.952836 20.710648 13.446667 23.552750 579110.610547 69788.531315 99319.074676 19738.731002 5472.352624 21828.765633 5118.275265 4739.135866 113469.458048
28 2031 690378.259364 46.708482 21.181927 14.011617 24.266963 NaN NaN NaN NaN NaN NaN NaN NaN NaN
29 2032 703109.475309 45.562913 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN
In [18]:
# Calculate the average for each corresponding cell in the three dataframes

Average1 = (Final_Arima_Results1 + Final_LSTM_Results1 + Final_Prophet_Results1) / 3

print("Average1 DataFrame:")
Average1

Average1 DataFrame:
Out[18]:
year Total Energy Consumption, Agriculture, Forestry, Animal Husbandry and Fishery(10000 tons of SCE) Total Energy Consumption, Manufacturing(10000 tons of SCE) Total Energy Consumption, Manufacture of Computers, Communication and Other Electronic Equipment(10000 tons of SCE) Total Energy Consumption, Wholesale, Retail Trade and Hotel, Restaurants(10000 tons of SCE)
0 2003.0 5683.210000 111222.870000 1098.680000 4723.400000
1 2004.0 6391.860000 136407.850000 1316.210000 5498.790000
2 2005.0 6860.460000 158234.920000 1517.970000 5916.630000
3 2006.0 7153.520000 174920.090000 1886.510000 6358.180000
4 2007.0 7068.450000 193133.070000 2096.360000 6731.960000
5 2008.0 6872.630000 198406.180000 2219.950000 6884.910000
6 2009.0 6978.210000 206555.600000 2228.680000 7303.220000
7 2010.0 7266.500000 217328.870000 2547.390000 7847.100000
8 2011.0 7675.230000 229090.990000 2650.410000 9147.500000
9 2012.0 7803.570000 234538.810000 2689.440000 10012.330000
10 2013.0 8054.800000 239053.400000 2801.590000 10598.160000
11 2014.0 8020.000000 248976.000000 2968.000000 10864.000000
12 2015.0 8271.000000 248264.000000 3149.000000 11447.000000
13 2016.0 8585.000000 247658.000000 3377.000000 12042.000000
14 2017.0 8945.000000 252462.000000 3662.000000 12456.000000
15 2018.0 8781.000000 258604.000000 4628.000000 12994.000000
16 2019.0 9018.000000 268426.000000 5028.000000 13624.000000
17 2020.0 9263.000000 279651.000000 5120.000000 13171.000000
18 2021.0 9481.567539 278522.038507 5361.747765 14167.904926
19 2022.0 9665.232297 282934.292368 5832.958457 14660.843232
20 2023.0 9803.879329 287043.327606 6389.149432 15164.928945
21 2024.0 10036.021723 291007.693016 6854.775635 15596.842323
22 2025.0 10253.871320 294303.054059 7464.092410 16056.412995
23 2026.0 10441.046755 296882.722568 8256.227256 16601.502994
24 2027.0 10601.538323 299953.909243 9107.472593 17087.584801
25 2028.0 10821.000574 302986.243368 9902.722982 17509.969207
26 2029.0 11039.507615 305710.560568 10697.532921 17976.630375
27 2030.0 11230.145226 308211.842699 11446.398236 18450.014970
In [19]:
import seaborn as sns
import matplotlib.pyplot as plt

# Convert 'Year' column to string
# Remove the last two characters from the 'year' column
Average1["year"] = Average1["year"].astype(str).str[:-2]

# Set seaborn style
sns.set(style="whitegrid")



# Iterate through each column and create separate plots
for col in Average1.columns[1:]:
    # Set the plot size
    plt.figure(figsize=(10, 6))
    ax = sns.lineplot(data=Average1, x="year", y=col, marker="o")
    ax.set_xlabel("Year")
    ax.set_ylabel("Values")
    ax.set_title(f"Average Plot for {col}")
    ax.yaxis.grid(True)  # Show only horizontal gridlines
    ax.xaxis.grid(False)  # Turn off vertical gridlines
    
    # Rotate x-axis tick labels by 45 degrees
    ax.set_xticklabels(ax.get_xticklabels(), rotation=45, ha="right")
    
    # Customize plot appearance
    sns.despine()  # Remove spines (axis lines)
    ax.tick_params(axis="both", which="both", length=0)  # Remove tick marks
    ax.set_facecolor("#f0f0f0")  # Set plot background color
    ax.grid(color="white", linestyle="-", linewidth=0.5)  # Adjust gridlines
    
    plt.tight_layout()  # Improve spacing between plots
    plt.show()
In [20]:
# Calculate the average for each corresponding cell
Average2 = (Final_Arima_Results2 + Final_LSTM_Results2 + Final_Prophet_Results2) / 3

print("Average2 Dataframe:")
Average2

Average2 Dataframe:
Out[20]:
year Total Energy Consumption(10000 tons of SCE) Proportion of Coal(%) Proportion of Petroleum(%) Proportion of Natural Gas(%) Proportion of Primary Electricity and Other Energy(%) Consumption of Coal(10000 tons) Consumption of Coke(10000 tons) Consumption of Crude Oil(10000 tons) Consumption of Gasoline(10000 tons) Consumption of Kerosene(10000 tons) Consumption of Diesel Oil(10000 tons) Consumption of Fuel Oil(10000 tons) Consumption of Natural Gas(100 million cu.m) Consumption of Electricity(100 million kwh)
0 2003.0 197083.000000 70.200000 20.100000 2.300000 7.400000 183760.240000 15926.470000 25180.720000 4118.520000 921.610000 8575.120000 4330.340000 339.080000 19031.600000
1 2004.0 230281.000000 70.200000 19.900000 2.300000 7.600000 212161.830000 18067.010000 29009.310000 4695.720000 1060.860000 10206.920000 4844.760000 396.720000 21971.370000
2 2005.0 261369.000000 72.400000 17.800000 2.400000 7.400000 243375.440000 25105.840000 30088.940000 4854.910000 1076.840000 10974.940000 4244.160000 466.080000 24940.320000
3 2006.0 286467.000000 72.400000 17.500000 2.700000 7.400000 270639.450000 28297.760000 32245.200000 5242.550000 1124.740000 11729.090000 4471.150000 573.320000 28587.970000
4 2007.0 311442.000000 72.500000 17.000000 3.000000 7.500000 290410.120000 31168.120000 34031.600000 5519.090000 1243.720000 12492.380000 4157.490000 705.230000 32711.810000
5 2008.0 320611.000000 71.500000 16.700000 3.400000 8.400000 300604.940000 32120.240000 35510.340000 6145.520000 1294.010000 13544.940000 3236.750000 812.940000 34541.350000
6 2009.0 336126.000000 71.600000 16.400000 3.500000 8.500000 325002.930000 36349.970000 38128.590000 6172.690000 1450.490000 13551.430000 2828.800000 895.200000 37032.140000
7 2010.0 360648.000000 69.200000 17.400000 4.000000 9.400000 349008.260000 38702.790000 42874.550000 6956.200000 1765.170000 14699.000000 3758.020000 1080.240000 41934.490000
8 2011.0 387043.000000 70.200000 16.800000 4.600000 8.400000 388961.100000 42063.280000 43965.840000 7595.950000 1816.720000 15635.100000 3662.800000 1341.070000 47000.880000
9 2012.0 402138.000000 68.500000 17.000000 4.800000 9.700000 411726.900000 44805.230000 46678.920000 8165.900000 1956.600000 16966.040000 3683.280000 1497.000000 49762.640000
10 2013.0 416913.000000 67.400000 17.100000 5.300000 10.200000 424425.940000 45851.870000 48652.150000 9366.350000 2164.070000 17150.650000 3953.970000 1705.370000 54203.410000
11 2014.0 428334.000000 65.800000 17.300000 5.600000 11.300000 413633.000000 46885.000000 51596.950000 9776.370000 2335.420000 17165.290000 4355.470000 1870.630000 57829.690000
12 2015.0 434113.000000 63.800000 18.400000 5.800000 12.000000 399834.000000 44059.000000 54788.280000 11368.460000 2663.710000 17360.310000 4662.010000 1931.750000 58019.980000
13 2016.0 441492.000000 62.200000 18.700000 6.100000 13.000000 388820.000000 45462.000000 57125.930000 11866.040000 2970.710000 16839.040000 4631.040000 2078.060000 61205.090000
14 2017.0 455827.000000 60.600000 18.900000 6.900000 13.600000 391403.000000 43743.000000 59402.170000 12296.270000 3326.360000 16916.540000 4887.300000 2393.690000 65913.970000
15 2018.0 471925.000000 59.000000 18.900000 7.600000 14.500000 397452.000000 43717.000000 63004.330000 13055.300000 3653.510000 16409.560000 4536.070000 2817.090000 71508.200000
16 2019.0 487488.000000 57.700000 19.000000 8.000000 15.300000 401915.000000 46426.000000 67268.270000 13627.970000 3950.230000 14917.950000 4690.340000 3059.680000 74866.120000
17 2020.0 498314.000000 56.900000 18.800000 8.400000 15.900000 404860.000000 48310.000000 69477.140000 12767.160000 3352.100000 14282.700000 5364.600000 3339.890000 77620.170000
18 2021.0 524000.000000 56.000000 18.500000 8.900000 16.600000 427258.939324 50677.440669 72487.120843 14091.509594 3891.820032 16137.937662 4782.566014 3505.296391 81121.216221
19 2022.0 541000.000000 56.200000 18.414099 9.549254 17.613554 432846.562047 52239.139613 75612.925957 14658.728012 4116.642342 15988.317240 4753.169750 3831.677435 85068.989276
20 2023.0 549132.750791 54.468516 18.339863 10.107691 18.388152 437483.357697 53503.731891 78938.779848 15264.378518 4332.502476 15799.938785 4731.049624 4180.033091 88888.651718
21 2024.0 562698.460287 53.639002 18.205704 10.592448 19.250497 442168.995153 54842.229741 82102.345724 15707.398007 4466.971396 15757.711861 4690.972069 4503.714572 92136.511659
22 2025.0 578001.716143 53.047491 18.087898 11.173894 20.067050 448342.320507 56075.767405 85227.170022 16261.993916 4672.242153 15754.111530 4670.122113 4868.842665 95890.169594
23 2026.0 592701.142570 52.340599 18.154348 11.810483 20.945044 453499.559007 57096.678915 88596.053262 16970.926527 4983.081390 15740.114481 4644.049997 5280.983881 99714.791608
24 2027.0 605536.587985 51.529700 18.394435 12.494767 21.874860 457515.878755 58072.807337 92036.914965 17569.563686 5211.505326 15593.189151 4654.064395 5725.174661 103520.795195
25 2028.0 616368.271189 50.723574 18.588178 13.081186 22.806079 461722.038601 59209.783212 95348.165736 18027.508632 5364.276818 15574.205066 4632.274460 6151.666200 106872.168157
26 2029.0 630696.861031 50.224041 18.739015 13.735974 23.664284 467475.611177 60390.426429 98682.763473 18596.857795 5592.630745 15531.265216 4631.617737 6585.838076 110641.120363
27 2030.0 644148.431186 49.613027 18.976984 14.414327 24.520892 472222.517695 61435.509328 102061.190983 19190.235718 5826.085054 15450.183857 4641.067163 7031.392216 114379.021022
28 2031.0 656784.901236 48.889980 19.271968 15.100218 25.364059 NaN NaN NaN NaN NaN NaN NaN NaN NaN
29 2032.0 667788.641682 48.210558 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN
In [21]:
import seaborn as sns
import matplotlib.pyplot as plt

# Convert 'Year' column to string
# Remove the last two characters from the 'year' column
Average2["year"] = Average2["year"].astype(str).str[:-2]

# Set seaborn style
sns.set(style="whitegrid")



# Iterate through each column and create separate plots
for col in Average2.columns[1:]:
    # Set the plot size
    plt.figure(figsize=(10, 6))
    ax = sns.lineplot(data=Average2, x="year", y=col, marker="o")
    ax.set_xlabel("Year")
    ax.set_ylabel("Values")
    ax.set_title(f"Average Plot for {col}")
    ax.yaxis.grid(True)  # Show only horizontal gridlines
    ax.xaxis.grid(False)  # Turn off vertical gridlines
    
    # Rotate x-axis tick labels by 45 degrees
    ax.set_xticklabels(ax.get_xticklabels(), rotation=45, ha="right")
    
    # Customize plot appearance
    sns.despine()  # Remove spines (axis lines)
    ax.tick_params(axis="both", which="both", length=0)  # Remove tick marks
    ax.set_facecolor("#f0f0f0")  # Set plot background color
    ax.grid(color="white", linestyle="-", linewidth=0.5)  # Adjust gridlines
    
    plt.tight_layout()  # Improve spacing between plots
    plt.show()
In [33]:
from sklearn.metrics import mean_squared_error
import numpy as np

# Assuming you have calculated your Average2 DataFrame
# And you have individual forecast DataFrames: Final_Arima_Results2, Final_LSTM_Results2, Final_Prophet_Results2
cols = ["Total Energy Consumption(10000 tons of SCE)",
        "Proportion of Coal(%)",
        "Proportion of Petroleum(%)", 
        "Proportion of Natural Gas(%)",
        "Proportion of Primary  Electricity and  Other Energy(%)",
        "Consumption of Coal(10000 tons)", 
        "Consumption of Coke(10000 tons)",
        "Consumption of Crude Oil(10000 tons)",
        "Consumption of Gasoline(10000 tons)",
        "Consumption of Kerosene(10000 tons)",
        "Consumption of Diesel Oil(10000 tons)",
        "Consumption of Fuel Oil(10000 tons)",
        "Consumption of Natural Gas(100 million cu.m)",
        "Consumption of Electricity(100 million kwh)"]

individual_forecasts = [Final_Arima_Results2, Final_LSTM_Results2, Final_Prophet_Results2]
forecast_names = ["ARIMA", "LSTM", "Prophet"]

for col in cols:
    forecast_columns = [forecast[col] for forecast in individual_forecasts]
    average_forecast_col = Average2[col]
    
    rmse_values = []
    
    for i, forecast_col in enumerate(forecast_columns):
        # Drop rows with NaN from both forecast_col and average_forecast_col
        valid_indices = ~np.isnan(forecast_col) & ~np.isnan(average_forecast_col)
        forecast_col_valid = forecast_col[valid_indices]
        average_forecast_col_valid = average_forecast_col[valid_indices]
        
        # Calculate the RMSE for the current forecast method
        rmse = np.sqrt(mean_squared_error(forecast_col_valid, average_forecast_col_valid))
        rmse_values.append(rmse)
        
        print(f"RMSE between Average and {forecast_names[i]} forecast for '{col}': {rmse:.2f}")

RMSE between Average and ARIMA forecast for 'Total Energy Consumption(10000 tons of SCE)': 8022.84
RMSE between Average and LSTM forecast for 'Total Energy Consumption(10000 tons of SCE)': 20774.90
RMSE between Average and Prophet forecast for 'Total Energy Consumption(10000 tons of SCE)': 12837.13
RMSE between Average and ARIMA forecast for 'Proportion of Coal(%)': 0.52
RMSE between Average and LSTM forecast for 'Proportion of Coal(%)': 0.55
RMSE between Average and Prophet forecast for 'Proportion of Coal(%)': 0.82
RMSE between Average and ARIMA forecast for 'Proportion of Petroleum(%)': 0.66
RMSE between Average and LSTM forecast for 'Proportion of Petroleum(%)': 0.39
RMSE between Average and Prophet forecast for 'Proportion of Petroleum(%)': 0.99
RMSE between Average and ARIMA forecast for 'Proportion of Natural Gas(%)': 0.87
RMSE between Average and LSTM forecast for 'Proportion of Natural Gas(%)': 1.23
RMSE between Average and Prophet forecast for 'Proportion of Natural Gas(%)': 0.36
RMSE between Average and ARIMA forecast for 'Proportion of Primary  Electricity and  Other Energy(%)': 1.34
RMSE between Average and LSTM forecast for 'Proportion of Primary  Electricity and  Other Energy(%)': 1.72
RMSE between Average and Prophet forecast for 'Proportion of Primary  Electricity and  Other Energy(%)': 0.38
RMSE between Average and ARIMA forecast for 'Consumption of Coal(10000 tons)': 24450.76
RMSE between Average and LSTM forecast for 'Consumption of Coal(10000 tons)': 19965.74
RMSE between Average and Prophet forecast for 'Consumption of Coal(10000 tons)': 44415.29
RMSE between Average and ARIMA forecast for 'Consumption of Coke(10000 tons)': 1432.95
RMSE between Average and LSTM forecast for 'Consumption of Coke(10000 tons)': 4855.01
RMSE between Average and Prophet forecast for 'Consumption of Coke(10000 tons)': 3448.60
RMSE between Average and ARIMA forecast for 'Consumption of Crude Oil(10000 tons)': 2297.26
RMSE between Average and LSTM forecast for 'Consumption of Crude Oil(10000 tons)': 3213.70
RMSE between Average and Prophet forecast for 'Consumption of Crude Oil(10000 tons)': 919.03
RMSE between Average and ARIMA forecast for 'Consumption of Gasoline(10000 tons)': 647.20
RMSE between Average and LSTM forecast for 'Consumption of Gasoline(10000 tons)': 428.36
RMSE between Average and Prophet forecast for 'Consumption of Gasoline(10000 tons)': 226.15
RMSE between Average and ARIMA forecast for 'Consumption of Kerosene(10000 tons)': 440.83
RMSE between Average and LSTM forecast for 'Consumption of Kerosene(10000 tons)': 578.95
RMSE between Average and Prophet forecast for 'Consumption of Kerosene(10000 tons)': 141.36
RMSE between Average and ARIMA forecast for 'Consumption of Diesel Oil(10000 tons)': 3111.66
RMSE between Average and LSTM forecast for 'Consumption of Diesel Oil(10000 tons)': 436.44
RMSE between Average and Prophet forecast for 'Consumption of Diesel Oil(10000 tons)': 2680.33
RMSE between Average and ARIMA forecast for 'Consumption of Fuel Oil(10000 tons)': 99.77
RMSE between Average and LSTM forecast for 'Consumption of Fuel Oil(10000 tons)': 155.57
RMSE between Average and Prophet forecast for 'Consumption of Fuel Oil(10000 tons)': 172.39
RMSE between Average and ARIMA forecast for 'Consumption of Natural Gas(100 million cu.m)': 262.10
RMSE between Average and LSTM forecast for 'Consumption of Natural Gas(100 million cu.m)': 1062.84
RMSE between Average and Prophet forecast for 'Consumption of Natural Gas(100 million cu.m)': 815.29
RMSE between Average and ARIMA forecast for 'Consumption of Electricity(100 million kwh)': 846.29
RMSE between Average and LSTM forecast for 'Consumption of Electricity(100 million kwh)': 1262.58
RMSE between Average and Prophet forecast for 'Consumption of Electricity(100 million kwh)': 436.45
In [34]:
from sklearn.metrics import mean_squared_error
import numpy as np

# Assuming you have calculated your Average2 DataFrame
# And you have individual forecast DataFrames: Final_Arima_Results2, Final_LSTM_Results2, Final_Prophet_Results2
cols = ["Total Energy Consumption, Agriculture, Forestry, Animal Husbandry and Fishery(10000 tons of SCE)", 
        "Total Energy Consumption, Manufacturing(10000 tons of SCE)", 
        "Total Energy Consumption, Manufacture of Computers, Communication and Other Electronic Equipment(10000 tons of SCE)",
        "Total Energy Consumption, Wholesale, Retail Trade and Hotel, Restaurants(10000 tons of SCE)"]

individual_forecasts = [Final_Arima_Results1, Final_LSTM_Results1, Final_Prophet_Results1]
forecast_names = ["ARIMA", "LSTM", "Prophet"]

for col in cols:  # Assuming 'cols' contains the forecasted column names
    # Select the corresponding columns from individual forecasts and the average forecast
    forecast_columns = [forecast[col] for forecast in individual_forecasts]
    average_forecast_col = Average1[col]
    
    rmse_values = []
    
    for i, forecast_col in enumerate(forecast_columns):
        # Drop rows with NaN from both forecast_col and average_forecast_col
        valid_indices = ~np.isnan(forecast_col) & ~np.isnan(average_forecast_col)
        forecast_col_valid = forecast_col[valid_indices]
        average_forecast_col_valid = average_forecast_col[valid_indices]
        
        # Calculate the RMSE for the current forecast method
        rmse = np.sqrt(mean_squared_error(forecast_col_valid, average_forecast_col_valid))
        rmse_values.append(rmse)
        
        print(f"RMSE between Average and {forecast_names[i]} forecast for '{col}': {rmse:.2f}")

RMSE between Average and ARIMA forecast for 'Total Energy Consumption, Agriculture, Forestry, Animal Husbandry and Fishery(10000 tons of SCE)': 83.17
RMSE between Average and LSTM forecast for 'Total Energy Consumption, Agriculture, Forestry, Animal Husbandry and Fishery(10000 tons of SCE)': 30.72
RMSE between Average and Prophet forecast for 'Total Energy Consumption, Agriculture, Forestry, Animal Husbandry and Fishery(10000 tons of SCE)': 54.36
RMSE between Average and ARIMA forecast for 'Total Energy Consumption, Manufacturing(10000 tons of SCE)': 2146.82
RMSE between Average and LSTM forecast for 'Total Energy Consumption, Manufacturing(10000 tons of SCE)': 4705.46
RMSE between Average and Prophet forecast for 'Total Energy Consumption, Manufacturing(10000 tons of SCE)': 5529.21
RMSE between Average and ARIMA forecast for 'Total Energy Consumption, Manufacture of Computers, Communication and Other Electronic Equipment(10000 tons of SCE)': 1290.92
RMSE between Average and LSTM forecast for 'Total Energy Consumption, Manufacture of Computers, Communication and Other Electronic Equipment(10000 tons of SCE)': 2896.80
RMSE between Average and Prophet forecast for 'Total Energy Consumption, Manufacture of Computers, Communication and Other Electronic Equipment(10000 tons of SCE)': 1610.87
RMSE between Average and ARIMA forecast for 'Total Energy Consumption, Wholesale, Retail Trade and Hotel, Restaurants(10000 tons of SCE)': 255.99
RMSE between Average and LSTM forecast for 'Total Energy Consumption, Wholesale, Retail Trade and Hotel, Restaurants(10000 tons of SCE)': 215.48
RMSE between Average and Prophet forecast for 'Total Energy Consumption, Wholesale, Retail Trade and Hotel, Restaurants(10000 tons of SCE)': 415.02
In [ ]: